summary of findings in research example quantitative

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]

• Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
• Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
• Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
• Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
• Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Cochrane Training

Chapter 14: completing ‘summary of findings’ tables and grading the certainty of the evidence.

Holger J Schünemann, Julian PT Higgins, Gunn E Vist, Paul Glasziou, Elie A Akl, Nicole Skoetz, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group (formerly Applicability and Recommendations Methods Group) and the Cochrane Statistical Methods Group

Key Points:

• A ‘Summary of findings’ table for a given comparison of interventions provides key information concerning the magnitudes of relative and absolute effects of the interventions examined, the amount of available evidence and the certainty (or quality) of available evidence.
• ‘Summary of findings’ tables include a row for each important outcome (up to a maximum of seven). Accepted formats of ‘Summary of findings’ tables and interactive ‘Summary of findings’ tables can be produced using GRADE’s software GRADEpro GDT.
• Cochrane has adopted the GRADE approach (Grading of Recommendations Assessment, Development and Evaluation) for assessing certainty (or quality) of a body of evidence.
• The GRADE approach specifies four levels of the certainty for a body of evidence for a given outcome: high, moderate, low and very low.
• GRADE assessments of certainty are determined through consideration of five domains: risk of bias, inconsistency, indirectness, imprecision and publication bias. For evidence from non-randomized studies and rarely randomized studies, assessments can then be upgraded through consideration of three further domains.

Cite this chapter as: Schünemann HJ, Higgins JPT, Vist GE, Glasziou P, Akl EA, Skoetz N, Guyatt GH. Chapter 14: Completing ‘Summary of findings’ tables and grading the certainty of the evidence. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook .

14.1 ‘Summary of findings’ tables

14.1.1 introduction to ‘summary of findings’ tables.

‘Summary of findings’ tables present the main findings of a review in a transparent, structured and simple tabular format. In particular, they provide key information concerning the certainty or quality of evidence (i.e. the confidence or certainty in the range of an effect estimate or an association), the magnitude of effect of the interventions examined, and the sum of available data on the main outcomes. Cochrane Reviews should incorporate ‘Summary of findings’ tables during planning and publication, and should have at least one key ‘Summary of findings’ table representing the most important comparisons. Some reviews may include more than one ‘Summary of findings’ table, for example if the review addresses more than one major comparison, or includes substantially different populations that require separate tables (e.g. because the effects differ or it is important to show results separately). In the Cochrane Database of Systematic Reviews (CDSR),  all ‘Summary of findings’ tables for a review appear at the beginning, before the Background section.

14.1.2 Selecting outcomes for ‘Summary of findings’ tables

Planning for the ‘Summary of findings’ table starts early in the systematic review, with the selection of the outcomes to be included in: (i) the review; and (ii) the ‘Summary of findings’ table. This is a crucial step, and one that review authors need to address carefully.

To ensure production of optimally useful information, Cochrane Reviews begin by developing a review question and by listing all main outcomes that are important to patients and other decision makers (see Chapter 2 and Chapter 3 ). The GRADE approach to assessing the certainty of the evidence (see Section 14.2 ) defines and operationalizes a rating process that helps separate outcomes into those that are critical, important or not important for decision making. Consultation and feedback on the review protocol, including from consumers and other decision makers, can enhance this process.

Critical outcomes are likely to include clearly important endpoints; typical examples include mortality and major morbidity (such as strokes and myocardial infarction). However, they may also represent frequent minor and rare major side effects, symptoms, quality of life, burdens associated with treatment, and resource issues (costs). Burdens represent the impact of healthcare workload on patient function and well-being, and include the demands of adhering to an intervention that patients or caregivers (e.g. family) may dislike, such as having to undergo more frequent tests, or the restrictions on lifestyle that certain interventions require (Spencer-Bonilla et al 2017).

Frequently, when formulating questions that include all patient-important outcomes for decision making, review authors will confront reports of studies that have not included all these outcomes. This is particularly true for adverse outcomes. For instance, randomized trials might contribute evidence on intended effects, and on frequent, relatively minor side effects, but not report on rare adverse outcomes such as suicide attempts. Chapter 19 discusses strategies for addressing adverse effects. To obtain data for all important outcomes it may be necessary to examine the results of non-randomized studies (see Chapter 24 ). Cochrane, in collaboration with others, has developed guidance for review authors to support their decision about when to look for and include non-randomized studies (Schünemann et al 2013).

If a review includes only randomized trials, these trials may not address all important outcomes and it may therefore not be possible to address these outcomes within the constraints of the review. Review authors should acknowledge these limitations and make them transparent to readers. Review authors are encouraged to include non-randomized studies to examine rare or long-term adverse effects that may not adequately be studied in randomized trials. This raises the possibility that harm outcomes may come from studies in which participants differ from those in studies used in the analysis of benefit. Review authors will then need to consider how much such differences are likely to impact on the findings, and this will influence the certainty of evidence because of concerns about indirectness related to the population (see Section 14.2.2 ).

Non-randomized studies can provide important information not only when randomized trials do not report on an outcome or randomized trials suffer from indirectness, but also when the evidence from randomized trials is rated as very low and non-randomized studies provide evidence of higher certainty. Further discussion of these issues appears also in Chapter 24 .

14.1.3 General template for ‘Summary of findings’ tables

Several alternative standard versions of ‘Summary of findings’ tables have been developed to ensure consistency and ease of use across reviews, inclusion of the most important information needed by decision makers, and optimal presentation (see examples at Figures 14.1.a and 14.1.b ). These formats are supported by research that focused on improved understanding of the information they intend to convey (Carrasco-Labra et al 2016, Langendam et al 2016, Santesso et al 2016). They are available through GRADE’s official software package developed to support the GRADE approach: GRADEpro GDT (www.gradepro.org).

Standard Cochrane ‘Summary of findings’ tables include the following elements using one of the accepted formats. Further guidance on each of these is provided in Section 14.1.6 .

• A brief description of the population and setting addressed by the available evidence (which may be slightly different to or narrower than those defined by the review question).
• A brief description of the comparison addressed in the ‘Summary of findings’ table, including both the experimental and comparison interventions.
• A list of the most critical and/or important health outcomes, both desirable and undesirable, limited to seven or fewer outcomes.
• A measure of the typical burden of each outcomes (e.g. illustrative risk, or illustrative mean, on comparator intervention).
• The absolute and relative magnitude of effect measured for each (if both are appropriate).
• The numbers of participants and studies contributing to the analysis of each outcomes.
• A GRADE assessment of the overall certainty of the body of evidence for each outcome (which may vary by outcome).
• Space for comments.
• Explanations (formerly known as footnotes).

Ideally, ‘Summary of findings’ tables are supported by more detailed tables (known as ‘evidence profiles’) to which the review may be linked, which provide more detailed explanations. Evidence profiles include the same important health outcomes, and provide greater detail than ‘Summary of findings’ tables of both of the individual considerations feeding into the grading of certainty and of the results of the studies (Guyatt et al 2011a). They ensure that a structured approach is used to rating the certainty of evidence. Although they are rarely published in Cochrane Reviews, evidence profiles are often used, for example, by guideline developers in considering the certainty of the evidence to support guideline recommendations. Review authors will find it easier to develop the ‘Summary of findings’ table by completing the rating of the certainty of evidence in the evidence profile first in GRADEpro GDT. They can then automatically convert this to one of the ‘Summary of findings’ formats in GRADEpro GDT, including an interactive ‘Summary of findings’ for publication.

As a measure of the magnitude of effect for dichotomous outcomes, the ‘Summary of findings’ table should provide a relative measure of effect (e.g. risk ratio, odds ratio, hazard) and measures of absolute risk. For other types of data, an absolute measure alone (such as a difference in means for continuous data) might be sufficient. It is important that the magnitude of effect is presented in a meaningful way, which may require some transformation of the result of a meta-analysis (see also Chapter 15, Section 15.4 and Section 15.5 ). Reviews with more than one main comparison should include a separate ‘Summary of findings’ table for each comparison.

Figure 14.1.a provides an example of a ‘Summary of findings’ table. Figure 15.1.b  provides an alternative format that may further facilitate users’ understanding and interpretation of the review’s findings. Evidence evaluating different formats suggests that the ‘Summary of findings’ table should include a risk difference as a measure of the absolute effect and authors should preferably use a format that includes a risk difference .

A detailed description of the contents of a ‘Summary of findings’ table appears in Section 14.1.6 .

Figure 14.1.a Example of a ‘Summary of findings’ table

Summary of findings (for interactive version click here )

a All the stockings in the nine studies included in this review were below-knee compression stockings. In four studies the compression strength was 20 mmHg to 30 mmHg at the ankle. It was 10 mmHg to 20 mmHg in the other four studies. Stockings come in different sizes. If a stocking is too tight around the knee it can prevent essential venous return causing the blood to pool around the knee. Compression stockings should be fitted properly. A stocking that is too tight could cut into the skin on a long flight and potentially cause ulceration and increased risk of DVT. Some stockings can be slightly thicker than normal leg covering and can be potentially restrictive with tight foot wear. It is a good idea to wear stockings around the house prior to travel to ensure a good, comfortable fit. Participants put their stockings on two to three hours before the flight in most of the studies. The availability and cost of stockings can vary.

b Two studies recruited high risk participants defined as those with previous episodes of DVT, coagulation disorders, severe obesity, limited mobility due to bone or joint problems, neoplastic disease within the previous two years, large varicose veins or, in one of the studies, participants taller than 190 cm and heavier than 90 kg. The incidence for the seven studies that excluded high risk participants was 1.45% and the incidence for the two studies that recruited high-risk participants (with at least one risk factor) was 2.43%. We have used 10 and 30 per 1000 to express different risk strata, respectively.

c The confidence interval crosses no difference and does not rule out a small increase.

d The measurement of oedema was not validated (indirectness of the outcome) or blinded to the intervention (risk of bias).

e If there are very few or no events and the number of participants is large, judgement about the certainty of evidence (particularly judgements about imprecision) may be based on the absolute effect. Here the certainty rating may be considered ‘high’ if the outcome was appropriately assessed and the event, in fact, did not occur in 2821 studied participants.

f None of the other studies reported adverse effects, apart from four cases of superficial vein thrombosis in varicose veins in the knee region that were compressed by the upper edge of the stocking in one study.

Figure 14.1.b Example of alternative ‘Summary of findings’ table

14.1.4 Producing ‘Summary of findings’ tables

The GRADE Working Group’s software, GRADEpro GDT ( www.gradepro.org ), including GRADE’s interactive handbook, is available to assist review authors in the preparation of ‘Summary of findings’ tables. GRADEpro can use data on the comparator group risk and the effect estimate (entered by the review authors or imported from files generated in RevMan) to produce the relative effects and absolute risks associated with experimental interventions. In addition, it leads the user through the process of a GRADE assessment, and produces a table that can be used as a standalone table in a review (including by direct import into software such as RevMan or integration with RevMan Web), or an interactive ‘Summary of findings’ table (see help resources in GRADEpro).

14.1.5 Statistical considerations in ‘Summary of findings’ tables

14.1.5.1 dichotomous outcomes.

‘Summary of findings’ tables should include both absolute and relative measures of effect for dichotomous outcomes. Risk ratios, odds ratios and risk differences are different ways of comparing two groups with dichotomous outcome data (see Chapter 6, Section 6.4.1 ). Furthermore, there are two distinct risk ratios, depending on which event (e.g. ‘yes’ or ‘no’) is the focus of the analysis (see Chapter 6, Section 6.4.1.5 ). In the presence of a non-zero intervention effect, any variation across studies in the comparator group risks (i.e. variation in the risk of the event occurring without the intervention of interest, for example in different populations) makes it impossible for more than one of these measures to be truly the same in every study.

It has long been assumed in epidemiology that relative measures of effect are more consistent than absolute measures of effect from one scenario to another. There is empirical evidence to support this assumption (Engels et al 2000, Deeks and Altman 2001, Furukawa et al 2002). For this reason, meta-analyses should generally use either a risk ratio or an odds ratio as a measure of effect (see Chapter 10, Section 10.4.3 ). Correspondingly, a single estimate of relative effect is likely to be a more appropriate summary than a single estimate of absolute effect. If a relative effect is indeed consistent across studies, then different comparator group risks will have different implications for absolute benefit. For instance, if the risk ratio is consistently 0.75, then the experimental intervention would reduce a comparator group risk of 80% to 60% in the intervention group (an absolute risk reduction of 20 percentage points), but would also reduce a comparator group risk of 20% to 15% in the intervention group (an absolute risk reduction of 5 percentage points).

‘Summary of findings’ tables are built around the assumption of a consistent relative effect. It is therefore important to consider the implications of this effect for different comparator group risks (these can be derived or estimated from a number of sources, see Section 14.1.6.3 ), which may require an assessment of the certainty of evidence for prognostic evidence (Spencer et al 2012, Iorio et al 2015). For any comparator group risk, it is possible to estimate a corresponding intervention group risk (i.e. the absolute risk with the intervention) from the meta-analytic risk ratio or odds ratio. Note that the numbers provided in the ‘Corresponding risk’ column are specific to the ‘risks’ in the adjacent column.

For the meta-analytic risk ratio (RR) and assumed comparator risk (ACR) the corresponding intervention risk is obtained as:

As an example, in Figure 14.1.a , the meta-analytic risk ratio for symptomless deep vein thrombosis (DVT) is RR = 0.10 (95% CI 0.04 to 0.26). Assuming a comparator risk of ACR = 10 per 1000 = 0.01, we obtain:

For the meta-analytic odds ratio (OR) and assumed comparator risk, ACR, the corresponding intervention risk is obtained as:

Upper and lower confidence limits for the corresponding intervention risk are obtained by replacing RR or OR by their upper and lower confidence limits, respectively (e.g. replacing 0.10 with 0.04, then with 0.26, in the example). Such confidence intervals do not incorporate uncertainty in the assumed comparator risks.

When dealing with risk ratios, it is critical that the same definition of ‘event’ is used as was used for the meta-analysis. For example, if the meta-analysis focused on ‘death’ (as opposed to survival) as the event, then corresponding risks in the ‘Summary of findings’ table must also refer to ‘death’.

In (rare) circumstances in which there is clear rationale to assume a consistent risk difference in the meta-analysis, in principle it is possible to present this for relevant ‘assumed risks’ and their corresponding risks, and to present the corresponding (different) relative effects for each assumed risk.

The risk difference expresses the difference between the ACR and the corresponding intervention risk (or the difference between the experimental and the comparator intervention).

For the meta-analytic risk ratio (RR) and assumed comparator risk (ACR) the corresponding risk difference is obtained as (note that risks can also be expressed using percentage or percentage points):

As an example, in Figure 14.1.b the meta-analytic risk ratio is 0.41 (95% CI 0.29 to 0.55) for diarrhoea in children less than 5 years of age. Assuming a comparator group risk of 22.3% we obtain:

For the meta-analytic odds ratio (OR) and assumed comparator risk (ACR) the absolute risk difference is obtained as (percentage points):

Upper and lower confidence limits for the absolute risk difference are obtained by re-running the calculation above while replacing RR or OR by their upper and lower confidence limits, respectively (e.g. replacing 0.41 with 0.28, then with 0.55, in the example). Such confidence intervals do not incorporate uncertainty in the assumed comparator risks.

14.1.5.2 Time-to-event outcomes

Time-to-event outcomes measure whether and when a particular event (e.g. death) occurs (van Dalen et al 2007). The impact of the experimental intervention relative to the comparison group on time-to-event outcomes is usually measured using a hazard ratio (HR) (see Chapter 6, Section 6.8.1 ).

A hazard ratio expresses a relative effect estimate. It may be used in various ways to obtain absolute risks and other interpretable quantities for a specific population. Here we describe how to re-express hazard ratios in terms of: (i) absolute risk of event-free survival within a particular period of time; (ii) absolute risk of an event within a particular period of time; and (iii) median time to the event. All methods are built on an assumption of consistent relative effects (i.e. that the hazard ratio does not vary over time).

(i) Absolute risk of event-free survival within a particular period of time Event-free survival (e.g. overall survival) is commonly reported by individual studies. To obtain absolute effects for time-to-event outcomes measured as event-free survival, the summary HR can be used in conjunction with an assumed proportion of patients who are event-free in the comparator group (Tierney et al 2007). This proportion of patients will be specific to a period of time of observation. However, it is not strictly necessary to specify this period of time. For instance, a proportion of 50% of event-free patients might apply to patients with a high event rate observed over 1 year, or to patients with a low event rate observed over 2 years.

As an example, suppose the meta-analytic hazard ratio is 0.42 (95% CI 0.25 to 0.72). Assuming a comparator group risk of event-free survival (e.g. for overall survival people being alive) at 2 years of ACR = 900 per 1000 = 0.9 we obtain:

so that that 956 per 1000 people will be alive with the experimental intervention at 2 years. The derivation of the risk should be explained in a comment or footnote.

(ii) Absolute risk of an event within a particular period of time To obtain this absolute effect, again the summary HR can be used (Tierney et al 2007):

In the example, suppose we assume a comparator group risk of events (e.g. for mortality, people being dead) at 2 years of ACR = 100 per 1000 = 0.1. We obtain:

so that that 44 per 1000 people will be dead with the experimental intervention at 2 years.

(iii) Median time to the event Instead of absolute numbers, the time to the event in the intervention and comparison groups can be expressed as median survival time in months or years. To obtain median survival time the pooled HR can be applied to an assumed median survival time in the comparator group (Tierney et al 2007):

In the example, assuming a comparator group median survival time of 80 months, we obtain:

For all three of these options for re-expressing results of time-to-event analyses, upper and lower confidence limits for the corresponding intervention risk are obtained by replacing HR by its upper and lower confidence limits, respectively (e.g. replacing 0.42 with 0.25, then with 0.72, in the example). Again, as for dichotomous outcomes, such confidence intervals do not incorporate uncertainty in the assumed comparator group risks. This is of special concern for long-term survival with a low or moderate mortality rate and a corresponding high number of censored patients (i.e. a low number of patients under risk and a high censoring rate).

14.1.6 Detailed contents of a ‘Summary of findings’ table

14.1.6.1 table title and header.

The title of each ‘Summary of findings’ table should specify the healthcare question, framed in terms of the population and making it clear exactly what comparison of interventions are made. In Figure 14.1.a , the population is people taking long aeroplane flights, the intervention is compression stockings, and the control is no compression stockings.

The first rows of each ‘Summary of findings’ table should provide the following ‘header’ information:

Patients or population This further clarifies the population (and possibly the subpopulations) of interest and ideally the magnitude of risk of the most crucial adverse outcome at which an intervention is directed. For instance, people on a long-haul flight may be at different risks for DVT; those using selective serotonin reuptake inhibitors (SSRIs) might be at different risk for side effects; while those with atrial fibrillation may be at low (< 1%), moderate (1% to 4%) or high (> 4%) yearly risk of stroke.

Setting This should state any specific characteristics of the settings of the healthcare question that might limit the applicability of the summary of findings to other settings (e.g. primary care in Europe and North America).

Intervention The experimental intervention.

Comparison The comparator intervention (including no specific intervention).

14.1.6.2 Outcomes

The rows of a ‘Summary of findings’ table should include all desirable and undesirable health outcomes (listed in order of importance) that are essential for decision making, up to a maximum of seven outcomes. If there are more outcomes in the review, review authors will need to omit the less important outcomes from the table, and the decision selecting which outcomes are critical or important to the review should be made during protocol development (see Chapter 3 ). Review authors should provide time frames for the measurement of the outcomes (e.g. 90 days or 12 months) and the type of instrument scores (e.g. ranging from 0 to 100).

Note that review authors should include the pre-specified critical and important outcomes in the table whether data are available or not. However, they should be alert to the possibility that the importance of an outcome (e.g. a serious adverse effect) may only become known after the protocol was written or the analysis was carried out, and should take appropriate actions to include these in the ‘Summary of findings’ table.

The ‘Summary of findings’ table can include effects in subgroups of the population for different comparator risks and effect sizes separately. For instance, in Figure 14.1.b effects are presented for children younger and older than 5 years separately. Review authors may also opt to produce separate ‘Summary of findings’ tables for different populations.

Review authors should include serious adverse events, but it might be possible to combine minor adverse events as a single outcome, and describe this in an explanatory footnote (note that it is not appropriate to add events together unless they are independent, that is, a participant who has experienced one adverse event has an unaffected chance of experiencing the other adverse event).

Outcomes measured at multiple time points represent a particular problem. In general, to keep the table simple, review authors should present multiple time points only for outcomes critical to decision making, where either the result or the decision made are likely to vary over time. The remainder should be presented at a common time point where possible.

Review authors can present continuous outcome measures in the ‘Summary of findings’ table and should endeavour to make these interpretable to the target audience. This requires that the units are clear and readily interpretable, for example, days of pain, or frequency of headache, and the name and scale of any measurement tools used should be stated (e.g. a Visual Analogue Scale, ranging from 0 to 100). However, many measurement instruments are not readily interpretable by non-specialist clinicians or patients, for example, points on a Beck Depression Inventory or quality of life score. For these, a more interpretable presentation might involve converting a continuous to a dichotomous outcome, such as >50% improvement (see Chapter 15, Section 15.5 ).

14.1.6.3 Best estimate of risk with comparator intervention

Review authors should provide up to three typical risks for participants receiving the comparator intervention. For dichotomous outcomes, we recommend that these be presented in the form of the number of people experiencing the event per 100 or 1000 people (natural frequency) depending on the frequency of the outcome. For continuous outcomes, this would be stated as a mean or median value of the outcome measured.

Estimated or assumed comparator intervention risks could be based on assessments of typical risks in different patient groups derived from the review itself, individual representative studies in the review, or risks derived from a systematic review of prognosis studies or other sources of evidence which may in turn require an assessment of the certainty for the prognostic evidence (Spencer et al 2012, Iorio et al 2015). Ideally, risks would reflect groups that clinicians can easily identify on the basis of their presenting features.

An explanatory footnote should specify the source or rationale for each comparator group risk, including the time period to which it corresponds where appropriate. In Figure 14.1.a , clinicians can easily differentiate individuals with risk factors for deep venous thrombosis from those without. If there is known to be little variation in baseline risk then review authors may use the median comparator group risk across studies. If typical risks are not known, an option is to choose the risk from the included studies, providing the second highest for a high and the second lowest for a low risk population.

14.1.6.4 Risk with intervention

For dichotomous outcomes, review authors should provide a corresponding absolute risk for each comparator group risk, along with a confidence interval. This absolute risk with the (experimental) intervention will usually be derived from the meta-analysis result presented in the relative effect column (see Section 14.1.6.6 ). Formulae are provided in Section 14.1.5 . Review authors should present the absolute effect in the same format as the risks with comparator intervention (see Section 14.1.6.3 ), for example as the number of people experiencing the event per 1000 people.

For continuous outcomes, a difference in means or standardized difference in means should be presented with its confidence interval. These will typically be obtained directly from a meta-analysis. Explanatory text should be used to clarify the meaning, as in Figures 14.1.a and 14.1.b .

14.1.6.5 Risk difference

For dichotomous outcomes, the risk difference can be provided using one of the ‘Summary of findings’ table formats as an additional option (see Figure 14.1.b ). This risk difference expresses the difference between the experimental and comparator intervention and will usually be derived from the meta-analysis result presented in the relative effect column (see Section 14.1.6.6 ). Formulae are provided in Section 14.1.5 . Review authors should present the risk difference in the same format as assumed and corresponding risks with comparator intervention (see Section 14.1.6.3 ); for example, as the number of people experiencing the event per 1000 people or as percentage points if the assumed and corresponding risks are expressed in percentage.

For continuous outcomes, if the ‘Summary of findings’ table includes this option, the mean difference can be presented here and the ‘corresponding risk’ column left blank (see Figure 14.1.b ).

14.1.6.6 Relative effect (95% CI)

The relative effect will typically be a risk ratio or odds ratio (or occasionally a hazard ratio) with its accompanying 95% confidence interval, obtained from a meta-analysis performed on the basis of the same effect measure. Risk ratios and odds ratios are similar when the comparator intervention risks are low and effects are small, but may differ considerably when comparator group risks increase. The meta-analysis may involve an assumption of either fixed or random effects, depending on what the review authors consider appropriate, and implying that the relative effect is either an estimate of the effect of the intervention, or an estimate of the average effect of the intervention across studies, respectively.

14.1.6.7 Number of participants (studies)

This column should include the number of participants assessed in the included studies for each outcome and the corresponding number of studies that contributed these participants.

14.1.6.8 Certainty of the evidence (GRADE)

Review authors should comment on the certainty of the evidence (also known as quality of the body of evidence or confidence in the effect estimates). Review authors should use the specific evidence grading system developed by the GRADE Working Group (Atkins et al 2004, Guyatt et al 2008, Guyatt et al 2011a), which is described in detail in Section 14.2 . The GRADE approach categorizes the certainty in a body of evidence as ‘high’, ‘moderate’, ‘low’ or ‘very low’ by outcome. This is a result of judgement, but the judgement process operates within a transparent structure. As an example, the certainty would be ‘high’ if the summary were of several randomized trials with low risk of bias, but the rating of certainty becomes lower if there are concerns about risk of bias, inconsistency, indirectness, imprecision or publication bias. Judgements other than of ‘high’ certainty should be made transparent using explanatory footnotes or the ‘Comments’ column in the ‘Summary of findings’ table (see Section 14.1.6.10 ).

14.1.6.9 Comments

The aim of the ‘Comments’ field is to help interpret the information or data identified in the row. For example, this may be on the validity of the outcome measure or the presence of variables that are associated with the magnitude of effect. Important caveats about the results should be flagged here. Not all rows will need comments, and it is best to leave a blank if there is nothing warranting a comment.

14.1.6.10 Explanations

Detailed explanations should be included as footnotes to support the judgements in the ‘Summary of findings’ table, such as the overall GRADE assessment. The explanations should describe the rationale for important aspects of the content. Table 14.1.a lists guidance for useful explanations. Explanations should be concise, informative, relevant, easy to understand and accurate. If explanations cannot be sufficiently described in footnotes, review authors should provide further details of the issues in the Results and Discussion sections of the review.

Table 14.1.a Guidance for providing useful explanations in ‘Summary of findings’ (SoF) tables. Adapted from Santesso et al (2016)

14.2 Assessing the certainty or quality of a body of evidence

14.2.1 the grade approach.

The Grades of Recommendation, Assessment, Development and Evaluation Working Group (GRADE Working Group) has developed a system for grading the certainty of evidence (Schünemann et al 2003, Atkins et al 2004, Schünemann et al 2006, Guyatt et al 2008, Guyatt et al 2011a). Over 100 organizations including the World Health Organization (WHO), the American College of Physicians, the American Society of Hematology (ASH), the Canadian Agency for Drugs and Technology in Health (CADTH) and the National Institutes of Health and Clinical Excellence (NICE) in the UK have adopted the GRADE system ( www.gradeworkinggroup.org ).

Cochrane has also formally adopted this approach, and all Cochrane Reviews should use GRADE to evaluate the certainty of evidence for important outcomes (see MECIR Box 14.2.a ).

MECIR Box 14.2.a Relevant expectations for conduct of intervention reviews

For systematic reviews, the GRADE approach defines the certainty of a body of evidence as the extent to which one can be confident that an estimate of effect or association is close to the quantity of specific interest. Assessing the certainty of a body of evidence involves consideration of within- and across-study risk of bias (limitations in study design and execution or methodological quality), inconsistency (or heterogeneity), indirectness of evidence, imprecision of the effect estimates and risk of publication bias (see Section 14.2.2 ), as well as domains that may increase our confidence in the effect estimate (as described in Section 14.2.3 ). The GRADE system entails an assessment of the certainty of a body of evidence for each individual outcome. Judgements about the domains that determine the certainty of evidence should be described in the results or discussion section and as part of the ‘Summary of findings’ table.

The GRADE approach specifies four levels of certainty ( Figure 14.2.a ). For interventions, including diagnostic and other tests that are evaluated as interventions (Schünemann et al 2008b, Schünemann et al 2008a, Balshem et al 2011, Schünemann et al 2012), the starting point for rating the certainty of evidence is categorized into two types:

• randomized trials; and
• non-randomized studies of interventions (NRSI), including observational studies (including but not limited to cohort studies, and case-control studies, cross-sectional studies, case series and case reports, although not all of these designs are usually included in Cochrane Reviews).

There are many instances in which review authors rely on information from NRSI, in particular to evaluate potential harms (see Chapter 24 ). In addition, review authors can obtain relevant data from both randomized trials and NRSI, with each type of evidence complementing the other (Schünemann et al 2013).

In GRADE, a body of evidence from randomized trials begins with a high-certainty rating while a body of evidence from NRSI begins with a low-certainty rating. The lower rating with NRSI is the result of the potential bias induced by the lack of randomization (i.e. confounding and selection bias).

However, when using the new Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool (Sterne et al 2016), an assessment tool that covers the risk of bias due to lack of randomization, all studies may start as high certainty of the evidence (Schünemann et al 2018). The approach of starting all study designs (including NRSI) as high certainty does not conflict with the initial GRADE approach of starting the rating of NRSI as low certainty evidence. This is because a body of evidence from NRSI should generally be downgraded by two levels due to the inherent risk of bias associated with the lack of randomization, namely confounding and selection bias. Not downgrading NRSI from high to low certainty needs transparent and detailed justification for what mitigates concerns about confounding and selection bias (Schünemann et al 2018). Very few examples of where not rating down by two levels is appropriate currently exist.

The highest certainty rating is a body of evidence when there are no concerns in any of the GRADE factors listed in Figure 14.2.a . Review authors often downgrade evidence to moderate, low or even very low certainty evidence, depending on the presence of the five factors in Figure 14.2.a . Usually, certainty rating will fall by one level for each factor, up to a maximum of three levels for all factors. If there are very severe problems for any one domain (e.g. when assessing risk of bias, all studies were unconcealed, unblinded and lost over 50% of their patients to follow-up), evidence may fall by two levels due to that factor alone. It is not possible to rate lower than ‘very low certainty’ evidence.

Review authors will generally grade evidence from sound non-randomized studies as low certainty, even if ROBINS-I is used. If, however, such studies yield large effects and there is no obvious bias explaining those effects, review authors may rate the evidence as moderate or – if the effect is large enough – even as high certainty ( Figure 14.2.a ). The very low certainty level is appropriate for, but is not limited to, studies with critical problems and unsystematic clinical observations (e.g. case series or case reports).

Figure 14.2.a Levels of the certainty of a body of evidence in the GRADE approach. *Upgrading criteria are usually applicable to non-randomized studies only (but exceptions exist).

14.2.2 Domains that can lead to decreasing the certainty level of a body of evidence   

We now describe in more detail the five reasons (or domains) for downgrading the certainty of a body of evidence for a specific outcome. In each case, if no reason is found for downgrading the evidence, it should be classified as 'no limitation or not serious' (not important enough to warrant downgrading). If a reason is found for downgrading the evidence, it should be classified as 'serious' (downgrading the certainty rating by one level) or 'very serious' (downgrading the certainty grade by two levels). For non-randomized studies assessed with ROBINS-I, rating down by three levels should be classified as 'extremely' serious.

(1) Risk of bias or limitations in the detailed design and implementation

Our confidence in an estimate of effect decreases if studies suffer from major limitations that are likely to result in a biased assessment of the intervention effect. For randomized trials, these methodological limitations include failure to generate a random sequence, lack of allocation sequence concealment, lack of blinding (particularly with subjective outcomes that are highly susceptible to biased assessment), a large loss to follow-up or selective reporting of outcomes. Chapter 8 provides a discussion of study-level assessments of risk of bias in the context of a Cochrane Review, and proposes an approach to assessing the risk of bias for an outcome across studies as ‘Low’ risk of bias, ‘Some concerns’ and ‘High’ risk of bias for randomized trials. Levels of ‘Low’. ‘Moderate’, ‘Serious’ and ‘Critical’ risk of bias arise for non-randomized studies assessed with ROBINS-I ( Chapter 25 ). These assessments should feed directly into this GRADE domain. In particular, ‘Low’ risk of bias would indicate ‘no limitation’; ‘Some concerns’ would indicate either ‘no limitation’ or ‘serious limitation’; and ‘High’ risk of bias would indicate either ‘serious limitation’ or ‘very serious limitation’. ‘Critical’ risk of bias on ROBINS-I would indicate extremely serious limitations in GRADE. Review authors should use their judgement to decide between alternative categories, depending on the likely magnitude of the potential biases.

Every study addressing a particular outcome will differ, to some degree, in the risk of bias. Review authors should make an overall judgement on whether the certainty of evidence for an outcome warrants downgrading on the basis of study limitations. The assessment of study limitations should apply to the studies contributing to the results in the ‘Summary of findings’ table, rather than to all studies that could potentially be included in the analysis. We have argued in Chapter 7, Section 7.6.2 , that the primary analysis should be restricted to studies at low (or low and unclear) risk of bias where possible.

Table 14.2.a presents the judgements that must be made in going from assessments of the risk of bias to judgements about study limitations for each outcome included in a ‘Summary of findings’ table. A rating of high certainty evidence can be achieved only when most evidence comes from studies that met the criteria for low risk of bias. For example, of the 22 studies addressing the impact of beta-blockers on mortality in patients with heart failure, most probably or certainly used concealed allocation of the sequence, all blinded at least some key groups and follow-up of randomized patients was almost complete (Brophy et al 2001). The certainty of evidence might be downgraded by one level when most of the evidence comes from individual studies either with a crucial limitation for one item, or with some limitations for multiple items. An example of very serious limitations, warranting downgrading by two levels, is provided by evidence on surgery versus conservative treatment in the management of patients with lumbar disc prolapse (Gibson and Waddell 2007). We are uncertain of the benefit of surgery in reducing symptoms after one year or longer, because the one study included in the analysis had inadequate concealment of the allocation sequence and the outcome was assessed using a crude rating by the surgeon without blinding.

(2) Unexplained heterogeneity or inconsistency of results

When studies yield widely differing estimates of effect (heterogeneity or variability in results), investigators should look for robust explanations for that heterogeneity. For instance, drugs may have larger relative effects in sicker populations or when given in larger doses. A detailed discussion of heterogeneity and its investigation is provided in Chapter 10, Section 10.10 and Section 10.11 . If an important modifier exists, with good evidence that important outcomes are different in different subgroups (which would ideally be pre-specified), then a separate ‘Summary of findings’ table may be considered for a separate population. For instance, a separate ‘Summary of findings’ table would be used for carotid endarterectomy in symptomatic patients with high grade stenosis (70% to 99%) in which the intervention is, in the hands of the right surgeons, beneficial, and another (if review authors considered it relevant) for asymptomatic patients with low grade stenosis (less than 30%) in which surgery appears harmful (Orrapin and Rerkasem 2017). When heterogeneity exists and affects the interpretation of results, but review authors are unable to identify a plausible explanation with the data available, the certainty of the evidence decreases.

(3) Indirectness of evidence

Two types of indirectness are relevant. First, a review comparing the effectiveness of alternative interventions (say A and B) may find that randomized trials are available, but they have compared A with placebo and B with placebo. Thus, the evidence is restricted to indirect comparisons between A and B. Where indirect comparisons are undertaken within a network meta-analysis context, GRADE for network meta-analysis should be used (see Chapter 11, Section 11.5 ).

Second, a review may find randomized trials that meet eligibility criteria but address a restricted version of the main review question in terms of population, intervention, comparator or outcomes. For example, suppose that in a review addressing an intervention for secondary prevention of coronary heart disease, most identified studies happened to be in people who also had diabetes. Then the evidence may be regarded as indirect in relation to the broader question of interest because the population is primarily related to people with diabetes. The opposite scenario can equally apply: a review addressing the effect of a preventive strategy for coronary heart disease in people with diabetes may consider studies in people without diabetes to provide relevant, albeit indirect, evidence. This would be particularly likely if investigators had conducted few if any randomized trials in the target population (e.g. people with diabetes). Other sources of indirectness may arise from interventions studied (e.g. if in all included studies a technical intervention was implemented by expert, highly trained specialists in specialist centres, then evidence on the effects of the intervention outside these centres may be indirect), comparators used (e.g. if the comparator groups received an intervention that is less effective than standard treatment in most settings) and outcomes assessed (e.g. indirectness due to surrogate outcomes when data on patient-important outcomes are not available, or when investigators seek data on quality of life but only symptoms are reported). Review authors should make judgements transparent when they believe downgrading is justified, based on differences in anticipated effects in the group of primary interest. Review authors may be aided and increase transparency of their judgements about indirectness if they use Table 14.2.b available in the GRADEpro GDT software (Schünemann et al 2013).

(4) Imprecision of results

When studies include few participants or few events, and thus have wide confidence intervals, review authors can lower their rating of the certainty of the evidence. The confidence intervals included in the ‘Summary of findings’ table will provide readers with information that allows them to make, to some extent, their own rating of precision. Review authors can use a calculation of the optimal information size (OIS) or review information size (RIS), similar to sample size calculations, to make judgements about imprecision (Guyatt et al 2011b, Schünemann 2016). The OIS or RIS is calculated on the basis of the number of participants required for an adequately powered individual study. If the 95% confidence interval excludes a risk ratio (RR) of 1.0, and the total number of events or patients exceeds the OIS criterion, precision is adequate. If the 95% CI includes appreciable benefit or harm (an RR of under 0.75 or over 1.25 is often suggested as a very rough guide) downgrading for imprecision may be appropriate even if OIS criteria are met (Guyatt et al 2011b, Schünemann 2016).

(5) High probability of publication bias

The certainty of evidence level may be downgraded if investigators fail to report studies on the basis of results (typically those that show no effect: publication bias) or outcomes (typically those that may be harmful or for which no effect was observed: selective outcome non-reporting bias). Selective reporting of outcomes from among multiple outcomes measured is assessed at the study level as part of the assessment of risk of bias (see Chapter 8, Section 8.7 ), so for the studies contributing to the outcome in the ‘Summary of findings’ table this is addressed by domain 1 above (limitations in the design and implementation). If a large number of studies included in the review do not contribute to an outcome, or if there is evidence of publication bias, the certainty of the evidence may be downgraded. Chapter 13 provides a detailed discussion of reporting biases, including publication bias, and how it may be tackled in a Cochrane Review. A prototypical situation that may elicit suspicion of publication bias is when published evidence includes a number of small studies, all of which are industry-funded (Bhandari et al 2004). For example, 14 studies of flavanoids in patients with haemorrhoids have shown apparent large benefits, but enrolled a total of only 1432 patients (i.e. each study enrolled relatively few patients) (Alonso-Coello et al 2006). The heavy involvement of sponsors in most of these studies raises questions of whether unpublished studies that suggest no benefit exist (publication bias).

A particular body of evidence can suffer from problems associated with more than one of the five factors listed here, and the greater the problems, the lower the certainty of evidence rating that should result. One could imagine a situation in which randomized trials were available, but all or virtually all of these limitations would be present, and in serious form. A very low certainty of evidence rating would result.

Table 14.2.a Further guidelines for domain 1 (of 5) in a GRADE assessment: going from assessments of risk of bias in studies to judgements about study limitations for main outcomes across studies

Table 14.2.b Judgements about indirectness by outcome (available in GRADEpro GDT)

Intervention:

Comparator:

Direct comparison:

Final judgement about indirectness across domains:

14.2.3 Domains that may lead to increasing the certainty level of a body of evidence

Although NRSI and downgraded randomized trials will generally yield a low rating for certainty of evidence, there will be unusual circumstances in which review authors could ‘upgrade’ such evidence to moderate or even high certainty ( Table 14.3.a ).

• Large effects On rare occasions when methodologically well-done observational studies yield large, consistent and precise estimates of the magnitude of an intervention effect, one may be particularly confident in the results. A large estimated effect (e.g. RR >2 or RR <0.5) in the absence of plausible confounders, or a very large effect (e.g. RR >5 or RR <0.2) in studies with no major threats to validity, might qualify for this. In these situations, while the NRSI may possibly have provided an over-estimate of the true effect, the weak study design may not explain all of the apparent observed benefit. Thus, despite reservations based on the observational study design, review authors are confident that the effect exists. The magnitude of the effect in these studies may move the assigned certainty of evidence from low to moderate (if the effect is large in the absence of other methodological limitations). For example, a meta-analysis of observational studies showed that bicycle helmets reduce the risk of head injuries in cyclists by a large margin (odds ratio (OR) 0.31, 95% CI 0.26 to 0.37) (Thompson et al 2000). This large effect, in the absence of obvious bias that could create the association, suggests a rating of moderate-certainty evidence.  Note : GRADE guidance suggests the possibility of rating up one level for a large effect if the relative effect is greater than 2.0. However, if the point estimate of the relative effect is greater than 2.0, but the confidence interval is appreciably below 2.0, then some hesitation would be appropriate in the decision to rate up for a large effect. Another situation allows inference of a strong association without a formal comparative study. Consider the question of the impact of routine colonoscopy versus no screening for colon cancer on the rate of perforation associated with colonoscopy. Here, a large series of representative patients undergoing colonoscopy may provide high certainty evidence about the risk of perforation associated with colonoscopy. When the risk of the event among patients receiving the relevant comparator is known to be near 0 (i.e. we are certain that the incidence of spontaneous colon perforation in patients not undergoing colonoscopy is extremely low), case series or cohort studies of representative patients can provide high certainty evidence of adverse effects associated with an intervention, thereby allowing us to infer a strong association from even a limited number of events.
• Dose-response The presence of a dose-response gradient may increase our confidence in the findings of observational studies and thereby enhance the assigned certainty of evidence. For example, our confidence in the result of observational studies that show an increased risk of bleeding in patients who have supratherapeutic anticoagulation levels is increased by the observation that there is a dose-response gradient between the length of time needed for blood to clot (as measured by the international normalized ratio (INR)) and an increased risk of bleeding (Levine et al 2004). A systematic review of NRSI investigating the effect of cyclooxygenase-2 inhibitors on cardiovascular events found that the summary estimate (RR) with rofecoxib was 1.33 (95% CI 1.00 to 1.79) with doses less than 25mg/d, and 2.19 (95% CI 1.64 to 2.91) with doses more than 25mg/d. Although residual confounding is likely to exist in the NRSI that address this issue, the existence of a dose-response gradient and the large apparent effect of higher doses of rofecoxib markedly increase our strength of inference that the association cannot be explained by residual confounding, and is therefore likely to be both causal and, at high levels of exposure, substantial.  Note : GRADE guidance suggests the possibility of rating up one level for a large effect if the relative effect is greater than 2.0. Here, the fact that the point estimate of the relative effect is greater than 2.0, but the confidence interval is appreciably below 2.0 might make some hesitate in the decision to rate up for a large effect
• Plausible confounding On occasion, all plausible biases from randomized or non-randomized studies may be working to under-estimate an apparent intervention effect. For example, if only sicker patients receive an experimental intervention or exposure, yet they still fare better, it is likely that the actual intervention or exposure effect is larger than the data suggest. For instance, a rigorous systematic review of observational studies including a total of 38 million patients demonstrated higher death rates in private for-profit versus private not-for-profit hospitals (Devereaux et al 2002). One possible bias relates to different disease severity in patients in the two hospital types. It is likely, however, that patients in the not-for-profit hospitals were sicker than those in the for-profit hospitals. Thus, to the extent that residual confounding existed, it would bias results against the not-for-profit hospitals. The second likely bias was the possibility that higher numbers of patients with excellent private insurance coverage could lead to a hospital having more resources and a spill-over effect that would benefit those without such coverage. Since for-profit hospitals are likely to admit a larger proportion of such well-insured patients than not-for-profit hospitals, the bias is once again against the not-for-profit hospitals. Since the plausible biases would all diminish the demonstrated intervention effect, one might consider the evidence from these observational studies as moderate rather than low certainty. A parallel situation exists when observational studies have failed to demonstrate an association, but all plausible biases would have increased an intervention effect. This situation will usually arise in the exploration of apparent harmful effects. For example, because the hypoglycaemic drug phenformin causes lactic acidosis, the related agent metformin was under suspicion for the same toxicity. Nevertheless, very large observational studies have failed to demonstrate an association (Salpeter et al 2007). Given the likelihood that clinicians would be more alert to lactic acidosis in the presence of the agent and over-report its occurrence, one might consider this moderate, or even high certainty, evidence refuting a causal relationship between typical therapeutic doses of metformin and lactic acidosis.

14.3 Describing the assessment of the certainty of a body of evidence using the GRADE framework

Review authors should report the grading of the certainty of evidence in the Results section for each outcome for which this has been performed, providing the rationale for downgrading or upgrading the evidence, and referring to the ‘Summary of findings’ table where applicable.

Table 14.3.a provides a framework and examples for how review authors can justify their judgements about the certainty of evidence in each domain. These justifications should also be included in explanatory notes to the ‘Summary of Findings’ table (see Section 14.1.6.10 ).

Chapter 15, Section 15.6 , describes in more detail how the overall GRADE assessment across all domains can be used to draw conclusions about the effects of the intervention, as well as providing implications for future research.

Table 14.3.a Framework for describing the certainty of evidence and justifying downgrading or upgrading

14.4 Chapter information

Authors: Holger J Schünemann, Julian PT Higgins, Gunn E Vist, Paul Glasziou, Elie A Akl, Nicole Skoetz, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group (formerly Applicability and Recommendations Methods Group) and the Cochrane Statistical Methods Group

Acknowledgements: Andrew D Oxman contributed to earlier versions. Professor Penny Hawe contributed to the text on adverse effects in earlier versions. Jon Deeks provided helpful contributions on an earlier version of this chapter. For details of previous authors and editors of the Handbook , please refer to the Preface.

Funding: This work was in part supported by funding from the Michael G DeGroote Cochrane Canada Centre and the Ontario Ministry of Health.

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Research Findings – Types Examples and Writing Guide

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Research Findings

Definition:

Research findings refer to the results obtained from a study or investigation conducted through a systematic and scientific approach. These findings are the outcomes of the data analysis, interpretation, and evaluation carried out during the research process.

Types of Research Findings

There are two main types of research findings:

Qualitative Findings

Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants, themes that emerge from the data, and descriptions of experiences and phenomena.

Quantitative Findings

Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables, graphs, or charts.

Both qualitative and quantitative findings are important in research and can provide different insights into a research question or problem. Combining both types of findings can provide a more comprehensive understanding of a phenomenon and improve the validity and reliability of research results.

Parts of Research Findings

Research findings typically consist of several parts, including:

• Introduction: This section provides an overview of the research topic and the purpose of the study.
• Literature Review: This section summarizes previous research studies and findings that are relevant to the current study.
• Methodology : This section describes the research design, methods, and procedures used in the study, including details on the sample, data collection, and data analysis.
• Results : This section presents the findings of the study, including statistical analyses and data visualizations.
• Discussion : This section interprets the results and explains what they mean in relation to the research question(s) and hypotheses. It may also compare and contrast the current findings with previous research studies and explore any implications or limitations of the study.
• Conclusion : This section provides a summary of the key findings and the main conclusions of the study.
• Recommendations: This section suggests areas for further research and potential applications or implications of the study’s findings.

How to Write Research Findings

Writing research findings requires careful planning and attention to detail. Here are some general steps to follow when writing research findings:

• Organize your findings: Before you begin writing, it’s essential to organize your findings logically. Consider creating an outline or a flowchart that outlines the main points you want to make and how they relate to one another.
• Use clear and concise language : When presenting your findings, be sure to use clear and concise language that is easy to understand. Avoid using jargon or technical terms unless they are necessary to convey your meaning.
• Use visual aids : Visual aids such as tables, charts, and graphs can be helpful in presenting your findings. Be sure to label and title your visual aids clearly, and make sure they are easy to read.
• Use headings and subheadings: Using headings and subheadings can help organize your findings and make them easier to read. Make sure your headings and subheadings are clear and descriptive.
• Interpret your findings : When presenting your findings, it’s important to provide some interpretation of what the results mean. This can include discussing how your findings relate to the existing literature, identifying any limitations of your study, and suggesting areas for future research.
• Be precise and accurate : When presenting your findings, be sure to use precise and accurate language. Avoid making generalizations or overstatements and be careful not to misrepresent your data.
• Edit and revise: Once you have written your research findings, be sure to edit and revise them carefully. Check for grammar and spelling errors, make sure your formatting is consistent, and ensure that your writing is clear and concise.

Research Findings Example

Following is a Research Findings Example sample for students:

Title: The Effects of Exercise on Mental Health

Sample : 500 participants, both men and women, between the ages of 18-45.

Methodology : Participants were divided into two groups. The first group engaged in 30 minutes of moderate intensity exercise five times a week for eight weeks. The second group did not exercise during the study period. Participants in both groups completed a questionnaire that assessed their mental health before and after the study period.

Findings : The group that engaged in regular exercise reported a significant improvement in mental health compared to the control group. Specifically, they reported lower levels of anxiety and depression, improved mood, and increased self-esteem.

Conclusion : Regular exercise can have a positive impact on mental health and may be an effective intervention for individuals experiencing symptoms of anxiety or depression.

Applications of Research Findings

Research findings can be applied in various fields to improve processes, products, services, and outcomes. Here are some examples:

• Healthcare : Research findings in medicine and healthcare can be applied to improve patient outcomes, reduce morbidity and mortality rates, and develop new treatments for various diseases.
• Education : Research findings in education can be used to develop effective teaching methods, improve learning outcomes, and design new educational programs.
• Technology : Research findings in technology can be applied to develop new products, improve existing products, and enhance user experiences.
• Business : Research findings in business can be applied to develop new strategies, improve operations, and increase profitability.
• Public Policy: Research findings can be used to inform public policy decisions on issues such as environmental protection, social welfare, and economic development.
• Social Sciences: Research findings in social sciences can be used to improve understanding of human behavior and social phenomena, inform public policy decisions, and develop interventions to address social issues.
• Agriculture: Research findings in agriculture can be applied to improve crop yields, develop new farming techniques, and enhance food security.
• Sports : Research findings in sports can be applied to improve athlete performance, reduce injuries, and develop new training programs.

When to use Research Findings

Research findings can be used in a variety of situations, depending on the context and the purpose. Here are some examples of when research findings may be useful:

• Decision-making : Research findings can be used to inform decisions in various fields, such as business, education, healthcare, and public policy. For example, a business may use market research findings to make decisions about new product development or marketing strategies.
• Problem-solving : Research findings can be used to solve problems or challenges in various fields, such as healthcare, engineering, and social sciences. For example, medical researchers may use findings from clinical trials to develop new treatments for diseases.
• Policy development : Research findings can be used to inform the development of policies in various fields, such as environmental protection, social welfare, and economic development. For example, policymakers may use research findings to develop policies aimed at reducing greenhouse gas emissions.
• Program evaluation: Research findings can be used to evaluate the effectiveness of programs or interventions in various fields, such as education, healthcare, and social services. For example, educational researchers may use findings from evaluations of educational programs to improve teaching and learning outcomes.
• Innovation: Research findings can be used to inspire or guide innovation in various fields, such as technology and engineering. For example, engineers may use research findings on materials science to develop new and innovative products.

Purpose of Research Findings

The purpose of research findings is to contribute to the knowledge and understanding of a particular topic or issue. Research findings are the result of a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques.

The main purposes of research findings are:

• To generate new knowledge : Research findings contribute to the body of knowledge on a particular topic, by adding new information, insights, and understanding to the existing knowledge base.
• To test hypotheses or theories : Research findings can be used to test hypotheses or theories that have been proposed in a particular field or discipline. This helps to determine the validity and reliability of the hypotheses or theories, and to refine or develop new ones.
• To inform practice: Research findings can be used to inform practice in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners to make informed decisions and improve outcomes.
• To identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research.
• To contribute to policy development: Research findings can be used to inform policy development in various fields, such as environmental protection, social welfare, and economic development. By providing evidence-based recommendations, research findings can help policymakers to develop effective policies that address societal challenges.

Characteristics of Research Findings

Research findings have several key characteristics that distinguish them from other types of information or knowledge. Here are some of the main characteristics of research findings:

• Objective : Research findings are based on a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques. As such, they are generally considered to be more objective and reliable than other types of information.
• Empirical : Research findings are based on empirical evidence, which means that they are derived from observations or measurements of the real world. This gives them a high degree of credibility and validity.
• Generalizable : Research findings are often intended to be generalizable to a larger population or context beyond the specific study. This means that the findings can be applied to other situations or populations with similar characteristics.
• Transparent : Research findings are typically reported in a transparent manner, with a clear description of the research methods and data analysis techniques used. This allows others to assess the credibility and reliability of the findings.
• Peer-reviewed: Research findings are often subject to a rigorous peer-review process, in which experts in the field review the research methods, data analysis, and conclusions of the study. This helps to ensure the validity and reliability of the findings.
• Reproducible : Research findings are often designed to be reproducible, meaning that other researchers can replicate the study using the same methods and obtain similar results. This helps to ensure the validity and reliability of the findings.

Advantages of Research Findings

Research findings have many advantages, which make them valuable sources of knowledge and information. Here are some of the main advantages of research findings:

• Evidence-based: Research findings are based on empirical evidence, which means that they are grounded in data and observations from the real world. This makes them a reliable and credible source of information.
• Inform decision-making: Research findings can be used to inform decision-making in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners and policymakers to make informed decisions and improve outcomes.
• Identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research. This contributes to the ongoing development of knowledge in various fields.
• Improve outcomes : Research findings can be used to develop and implement evidence-based practices and interventions, which have been shown to improve outcomes in various fields, such as healthcare, education, and social services.
• Foster innovation: Research findings can inspire or guide innovation in various fields, such as technology and engineering. By providing new information and understanding of a particular topic, research findings can stimulate new ideas and approaches to problem-solving.
• Enhance credibility: Research findings are generally considered to be more credible and reliable than other types of information, as they are based on rigorous research methods and are subject to peer-review processes.

Limitations of Research Findings

While research findings have many advantages, they also have some limitations. Here are some of the main limitations of research findings:

• Limited scope: Research findings are typically based on a particular study or set of studies, which may have a limited scope or focus. This means that they may not be applicable to other contexts or populations.
• Potential for bias : Research findings can be influenced by various sources of bias, such as researcher bias, selection bias, or measurement bias. This can affect the validity and reliability of the findings.
• Ethical considerations: Research findings can raise ethical considerations, particularly in studies involving human subjects. Researchers must ensure that their studies are conducted in an ethical and responsible manner, with appropriate measures to protect the welfare and privacy of participants.
• Time and resource constraints : Research studies can be time-consuming and require significant resources, which can limit the number and scope of studies that are conducted. This can lead to gaps in knowledge or a lack of research on certain topics.
• Complexity: Some research findings can be complex and difficult to interpret, particularly in fields such as science or medicine. This can make it challenging for practitioners and policymakers to apply the findings to their work.
• Lack of generalizability : While research findings are intended to be generalizable to larger populations or contexts, there may be factors that limit their generalizability. For example, cultural or environmental factors may influence how a particular intervention or treatment works in different populations or contexts.

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• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.

Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:

“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

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I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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CHAPTER FOUR DATA ANALYSIS AND PRESENTATION OF RESEARCH FINDINGS 4.1 Introduction

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• Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)

After you have designed your study, collected your data, and analyzed it, you have to figure out what it means and communicate that to potential interested audiences. This section of the chapter is about how to make sense of your study, in terms of data interpretation, data write-up, and data presentation, as seen in the above diagram.

• Chapter One: Introduction
• Chapter Two: Understanding the distinctions among research methods
• Chapter Three: Ethical research, writing, and creative work
• Chapter Four: Quantitative Methods (Part 1)
• Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
• Chapter Five: Qualitative Methods (Part 1)
• Chapter Five: Qualitative Data (Part 2)
• Chapter Six: Critical / Rhetorical Methods (Part 1)
• Chapter Six: Critical / Rhetorical Methods (Part 2)
• Chapter Seven: Presenting Your Results

Data Interpretation

Once you have run your statistics, you have to figure out what your findings mean or interpret your data. To do this, you need to tie back your findings to your research questions and/or hypotheses, think about how your findings relate to what you discovered beforehand about the already existing literature, and determine how your findings take the literature or current theory in the field further. Your interpretation of the data you collected will be found in the last section of your paper, what is commonly called the "discussion" section.

Remember Your RQs/Hs

Your research questions and hypotheses, once developed, should guide your study throughout the research process. As you are choosing your research design, choosing how to operationalize your variables, and choosing/conducting your statistical tests, you should always keep your RQs and Hs in mind.

What were you wanting to discover by your study? What were you wanting to test? Make sure you answer these questions clearly for the reader of your study in both the results and discussion section of the paper. (Specific guidelines for these sections will be covered later in this chapter, including the common practice of placing the data as you present it with each research question in the results section.)

Tie Findings to Your Literature Review

As you have seen in chapter 3 and the Appendix, and will see in chapter 7, the literature review is what you use to set up your quantitative study and to show why there is a need for your study. It should start out broad, with the context for your study, and lead into showing what still needs to be known and studied about your topic area, justifying your focus in the study. It will be brought in again in the last section of the paper you write, i.e., the discussion section.

Your paper is like an hourglass – starting out broad and narrowing down in the middle with your actual study and findings, and then moving to broad implications for the larger context of your study near the end.

Think about Relationship of Findings to Theory

One of the things you will write about in your discussion or last section of your paper is the implications of what you found. These implications are both practical and theoretical. Practical implications are how the research can provide practical applications to real-world people and issues. Theoretical implications are how the research takes the current academic literature further, specifically, in relationship to theory-building.

Did any of the research you reviewed for your literature review mention a theory your findings could expand upon? If so, you should think about how your findings related to this theory. If not, then think about the theories you have already studied in your communication classes. Would any of them provide a possible explanation of what you found? Would your findings help expand that theory to a different context, the context you studied? Does a theory need to be developed in the area of your research? If so, then what aspects of that theory could your findings help explain?

Data Write-Up

All quantitative studies, when written, have four parts. The first part is the introduction and literature review, the second part is the methods section, the third section is the results or findings, and the fourth section is the discussion section. This portion of this chapter will explain what elements you will need to include in each of these sections.

Literature Review

The beginning of your paper and first few pages sets the tone for your study. It tells the reader what the context of your study is and what other people who are also interested in your topic have studied about your topic.

There are many ways to organize a literature review, as can be seen in the following website. Literature Reviews — The Writing Center at UNC-Chapel Hill

After you have done a thorough literature search on your topic, then you have to organize your literature into topics of some kind. Your main goal is to show what has been done and what still needs to be done, to show the need for your study, so at the end of each section of your literature review, you should identify what still needs to be known about that particular area.

For quantitative research, you should do your literature review before coming up with your research questions/hypotheses. Your questions and hypotheses should flow from the literature. This is different from the other two research methods discussed in this book, which do not rely so heavily on a literature review to situation the study before conducting it.

In the methods section, you should tell your reader how you conducted your study, from start to finish, explaining why you made the choices you did along the way. A reader should be able to replicate your study from the descriptions you provide in this section of your write-up. Common headings in the methods section include a description of the participants, procedures, and analysis.

Participants

For the participants' subheading of the methods section, you should minimally report the demographics of your sample in terms of biological sex (frequencies/percentages), age (range of ages and mean), and ethnicity (frequencies/percentages). If you collected data on other demographics, such as socioeconomic status, religious affiliation, type of occupation, etc., then you can report data for that also in the participants' sub-section.

For the procedures sub-section, you report everything you did to collect your data: how you recruited your participants, including what type of sampling you used (probability or non-probability) and informed consent procedures; how you operationalized your variables (including your survey questions, which often are explained in the methods section briefly while the whole survey can be found in an appendix of your paper); the validity and reliability of your survey instrument or methods you used; and what type of study design you had (experimental, quasi-experimental, or non-experimental). For each one of these design issues, in this sub-section of the methods part, you need to explain why you made the decisions you did in order to answer your research questions or test your hypotheses.

In this section, you explain how you converted your data for analysis and how you analyzed your data. You need to explain what statistics you chose to run for each of your research questions/hypotheses and why.

In this section of your paper, you organize the results by your research questions/hypotheses. For each research question/hypothesis, you should present any descriptive statistic results first and then your inferential statistics results. You do not make any interpretation of what your results mean or why you think you got the results you did. You merely report your results.

Reporting Significant Results

For each of the inferential statistics, there is a typical template you can follow when reporting significant results: reporting the test statistic value, the degrees of freedom  3 , and the probability level. Examples follow for each of the statistics we have talked about in this text.

T-test results

"T-tests results show there was a significant difference found between men and women on their levels of self-esteem,  t  (df) = t value,  p  < .05, with men's self-esteem being higher (or lower) (men's mean & standard deviation) than women's self-esteem (women's mean & standard deviation)."

ANOVA results

"ANOVA results indicate there was a significant difference found between [levels of independent variable] on [dependent variable],  F  (df) = F value,  p  < .05."

If doing a factorial ANOVA, you would report the above sentence for all of your independent variables (main effects), as well as for the interaction (interaction effect), with language something like: "ANOVA results indicate a significant main effect for [independent variable] on [dependent variable],  F  (df) = F value,  p  < .05. .... ANOVA results indicate a significant interaction effect between [independent variables] on [dependent variable],  F  (df) = F value,  p  < .05."

See example YouTube tutorial for writing up a two-way ANOVA at the following website.

Factorial Design (Part C): Writing Up Results

Chi-square results

For goodness of fit results, your write-up would look something like: "Using a chi-square goodness of fit test, there was a significant difference found between observed and expected values of [variable], χ2 (df) = chi-square value,  p  < .05." For test of independence results, it would like like: "Using a chi-square test of independence, there was a significant interaction between [your two variables], χ2 (df) = chi-square value,  p  < .05."

Correlation results

"Using Pearson's [or Spearman's] correlation coefficient, there was a significant relationship found between [two variables],  r  (df) = r value,  p  < .05." If there are a lot of significant correlation results, these results are often presented in a table form.

For more information on these types of tables, see the following website:  Correlation Tables .

Regression results

Reporting regression results is more complicated, but generally, you want to inform the reader about how much variance is accounted by the regression model, the significance level of the model, and the significance of the predictor variable. For example:

A regression analysis, predicting GPA scores from GRE scores, was statistically significant,  F (1,8) = 10.34,  p  < .05.

Coefficientsa

The regression equation is: Ŷ = .411 * .005X. For every one unit increase in GRE score, there is a corresponding increase in GPA of .005 (Walen-Frederick, n.d., p. 4).

For more write-up help on regression and other statistics, see the following website location:

Multiple Regression  (pp. 217-220)

Reporting Non-Significant Results

You can follow a similar template when reporting non-significant results for all of the above inferential statistics. It is the same as provided in the above examples, except the word "non-significant" replaces the word "significant," and the  p  values are adjusted to indicate  p > .05.

Many times readers of articles do not read the whole article, especially if they are afraid of the statistical sections. When this happens, they often read the discussion section, which makes this a very important section in your writing. You should include the following elements in your discussion section: (a) a summary of your findings, (b) implications, (c) limitations, and (d) future research ideas.

Summary of Findings

You should summarize the answers to your research questions or what you found when testing your hypotheses in this sub-section of the discussion section. You should not report any statistical data here, but just put your results into narrative form. What did you find out that you did not know before doing your study? Answer that question in this sub- section.

Implications

You need to indicate why your study was important, both theoretically and practically. For the theoretical implications, you should relate what you found to the already existing literature, as discussed earlier when the "hourglass" format was mentioned as a way of conceptualizing your whole paper. If your study added anything to the existing theory on a particular topic, you talk about this here as well.

For practical implications, you need to identify for the reader how this study can help people in their real-world experiences related to your topic. You do not want your study to just be important to academic researchers, but also to other professionals and persons interested in your topic.

Limitations

As you get through conducting your study, you are going to realize there are things you wish you had done differently. Rather than hide these things from the reader, it is better to forthrightly state these for the reader. Explain why your study is limited and what you wish you had done in this sub-section.

Future Research

The limitations sub-section usually is tied directly to the future research sub-section, as your limitations mean that future research should be done to deal with these limitations. There may also be other things that could be studied, however, as a result of what you have found. What would other people say are the "gaps" your study left unstudied on your topic? These should be identified, with some suggestions on how they might be studied.

Other Aspects of the Paper

There are other parts of the academic paper you should include in your final write-up. We have provided useful resources for you to consider when including these aspects as part of your paper. For an example paper that uses the required APA format for a research paper write-up, see the following source:  Varying Definitions of Online Communication .

Abstract & Titles.

Research Abstracts General Format

Tables, References, & Other Materials.

APA Tables and Figures 1 Reference List: Basic Rules

Data Presentation

You will probably be called upon to present your data in other venues besides in writing. Two of the most common venues are oral presentations such as in class or at conferences, and poster presentations, such as what you might find at conferences. You might also be called upon to not write an academic write-up of your study, but rather to provide an executive summary of the results of your study to the "powers that be," who do not have time to read more than 5 pages or so of a summary. There are good resources for doing all of these online, so we have provided these here.

Oral Presentations

Oral Presentations Delivering Presentations

Poster Presentations

Executive Summary

Executive Summaries Complete the Report Good & Poor Examples of Executive Summaries with the following link: http://unilearning.uow.edu.au/report/4bi1.html

Congratulations! You have learned a great deal about how to go about using quantitative methods for your future research projects. You have learned how to design a quantitative study, conduct a quantitative study, and write about a quantitative study. You have some good resources you can take with you when you leave this class. Now, you just have to apply what you have learned to projects that will come your way in the future.

Remember, just because you may not like one method the best does not mean you should not use it. Your research questions/hypotheses should ALWAYS drive your choice of which method you use. And remember also that you can do quantitative methods!

[NOTE: References are not provided for the websites cited in the text, even though if this was an actual research article, they would need to be cited.]

Baker, E., Baker, W., & Tedesco, J. C. (2007). Organizations respond to phishing: Exploring the public relations tackle box.  Communication Research Reports, 24  (4), 327-339.

Benoit, W. L., & Hansen, G. J. (2004). Presidential debate watching, issue knowledge, character evaluation, and vote choice.  Human Communication Research, 30  (1), 121-144.

Chatham, A. (1991).  Home vs. public schooling: What about relationships in adolescence? Doctoral dissertation, University of Oklahoma.

Cousineau, T. M., Rancourt, D., and Green, T. C. (2006). Web chatter before and after the women's health initiative results: A content analysis of on-line menopause message boards.  Journal of Health Communication, 11 (2), 133-147.

Derlega, V., Winstead, B. A., Mathews, A., and Braitman, A. L. (2008). Why does someone reveal highly personal information?: Attributions for and against self-disclosure in close relationships.  Communication Research Reports, 25 , 115-130.

Fischer, J., & Corcoran, K. (2007).  Measures for clinical practice and research: A sourcebook (volumes 1 & 2) . New York: Oxford University Press.

Guay, S., Boisvert, J.-M., & Freeston, M. H. (2003). Validity of three measures of communication for predicting relationship adjustment and stability among a sample of young couples.  Psychological Assessment , 15(3), 392-398.

Holbert, R. L., Tschida, D. A., Dixon, M., Cherry, K., Steuber, K., & Airne, D. (2005). The  West Wing  and depictions of the American Presidency: Expanding the domains of framing in political communication.  Communication Quarterly, 53  (4), 505-522.

Jensen, J. D. (2008). Scientific uncertainty in news coverage of cancer research: Effects of hedging on scientists' and journalists' credibility.  Human Communication Research, 34 , 347- 369.

Keyton, J. (2011).  Communicating research: Asking questions, finding answers . New York: McGraw Hill.

Lenhart, A., Ling, R., Campbell, S., & Purcell, K. (2010, Apr. 10).  Teens and mobile phones . Report from the Pew Internet and American Life Project, retrieved from  http://www.pewinternet.org/Reports/2010/Teens-and-Mobile-Phones.aspx .

Maddy, T. (2008).  Tests: A comprehensive reference for assessments in psychology, education, and business . Austin, TX: Pro-Ed.

McCollum Jr., J. F., & Bryant, J. (2003). Pacing in children's television programming.  Mass Communication and Society, 6  (2), 115-136.

Medved, C. E., Brogan, S. M., McClanahan, A. M., Morris, J. F., & Shepherd, G. J. (2006). Family and work socializing communication: Messages, gender, and ideological implications.  Journal of Family Communication, 6 (3), 161-180.

Moyer-Gusé, E., & Nabi, R. L. (2010). Explaining the effects of narrative in an entertainment television program: Overcoming resistance to persuasion.  Human Communication Research, 36 , 26-52.

Nabi, R. L. (2009). Cosmetic surgery makeover programs and intentions to undergo cosmetic enhancements: A consideration of three models of media effects.  Human Communication Research, 35 , 1-27.

Pearson, J. C., DeWitt, L., Child, J. T., Kahl Jr., D. H., and Dandamudi, V. (2007). Facing the fear: An analysis of speech-anxiety content in public-speaking textbooks.  Communication Research Reports, 24 (2), 159-168.

Rubin. R. B., Rubin, A. M., Graham, E., Perse, E. M., & Seibold, D. (2009).  Communication research measures II: A sourcebook . New York: Routledge.

Serota, K. B., Levine, T. R., and Boster, F. J. (2010). The prevalence of lying in America: Three studies of reported deception.  Human Communication Research, 36 , 1-24.

Sheldon, P. (2008). The relationship between unwillingness-to-communicate and students' facebook use.  Journal of Media Psychology, 20 (2), 67–75.

Trochim, W. M. K. (2006). Reliability and validity.  Research methods data base , retrieved from  http://www.socialresearchmethods.net/kb/relandval.php .

Walen-Frederick, H. (n.d.).  Help sheet for reading SPSS printouts . Retrieved from  http://www.scribd.com/doc/51982223/help-sheet-for-reading-spss-printouts .

Weaver, A. J., & Wilson, B. J. (2009). The role of graphic and sanitized violence in the enjoyment of television dramas.  Human Communication Research, 35 (3), 442-463.

Weber, K., Corrigan, M., Fornash, B., & Neupauer, N. C. (2003). The effect of interest on recall: An experiment.  Communication Research Reports, 20 (2), 116-123.

Witt, P. L., & Schrodt, P. (2006). The influence of instructional technology use and teacher immediacy on student affect for teacher and course.  Communication Reports, 19 (1), 1-15.

3 Degrees of freedom (df) relate to your sample size and to the number of groups being compared. SPSS always computes the df for your statistics. For more information on degrees of freedom, see the following web-based resources:  http://www.youtube.com/watch?v=wsvfasNpU2s  and  http://www.creative-wisdom.com/pub/df/index.htm .

Back to Previous Spot

Improved Surface Drainage of Pavements: Final Report (1998)

Chapter: chapter 5 summary, findings, and recommendations.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

CHAPIER 5 SI~MARY, FINDINGS, AND RECOMMENDATIONS SUGARY The primary objective of this research was to identify unproved methods for draining rainwater from the surface of multi-lane pavements and to develop guidelines for their use. The guidelines, along with details on the rationale for their development, are presented in a separate document' "Proposed Design Guidelines for Improving Pavement Surface Drainage" (2J. The guidelines support an interactive computer program, PAVDRN, that can be used by practicing engineers In the process of designing new pavements or rehabilitating old pavements' is outlined In figure 39. The intended audience for the guidelines is practicing highway design engineers that work for transportation agencies or consulting firms. Improved pavement surface drainage is needed for two reasons: (~) to minimize splash and spray and (2) to control the tendency for hydroplaning. Both issues are primary safety concerns. At the request of the advisory panel for the project, the main focus of this study was on ~mprov~g surface drainage to mammae the tendency for hydroplaning. In terms of reducing the tendency for hydroplaTuT g, the needed level of drainage is defined in terms of the thickness of the film of water on the pavement. Therefore, the guidelines were developed within the context of reducing the thickness of the water film on pavement surfaces to the extent that hydroplaning is unlikely at highway design speeds. Since hydroplaning is ~7

DESIGN CRITERIA Pavement Geometry Number of lanes Section type - Tangent - Horizontal curve - Transition - Vertical crest curve - Vertical sag curve Enviromnental oramaters Rainfall intensity ~ Temperature Pavement Tvpe Dense-graded asphalt Porous asphalt Portland cement concrete ~ Grooved Portland cement concrete Desion Soeed Allowable speed for onset of hydroplaning Recommend Desion Changes Alter geometry Alter pavement surface Add appurtenances Groove (Portland cement concrete) CALCULATIONS Lenoth of flow path Calculate on basis of pavement geometry IT Hydraulic Analvses .  No? Water film thickness Equation No. 10 Equation No.'s. 16-19 1 Hvdroolanino Analvsis Hydroplaning speed Equation No.'s 21-24 Rainfall Intensity Equation No. 25 -A I / Meet Design ~ \ Cntena? / \<es? Accent Desinn | Figure 39. Flow diagram representing PAVI)RN design process In "Proposed Guidelines for Improving Pavement Surface DrmT~age" (2). 118

controlled primarily by the thickness of the water film on the pavement surface, the design guidelines focus on the prediction and control of ache depth of water flowing across the pavement surface as a result of rainfall, often referred to as sheet flow. Water film thickness on highway pavements can be controlled In three fundamental ways, by: I. Minimizing the length of the longest flow path of the water over We pavement and thereby the distance over which the flow can develop; 2. Increasing the texture of the pavement surface; and 3. Removing water from the pavement's surface. In the process of using PAVDRN to implement the design guidelines, the designer is guided to (~) minimize the longest drainage path length of the section under design by altering the pavement geometry and (2) reduce the resultant water film thickness that will develop along that drainage path length by increasing the mean texture depth, choosing a surface that maximizes texture, or using permeable pavements, grooving, and appurtenances to remove water from the surface. Through the course of a typical design project, four key areas need to be considered in order to analyze and eventually reduce the potential for hydroplaning. These areas are: ~9

I. Environmental conditions: 2. Geometry of the roadway surface; 3. Pavement surface (texture) properties; and 4. Appurtenances. Each of these areas and their influence on the resulting hydroplaning speed of the designed section are discussed In detail In the guidelines (21. The environmental conditions considered are rainfall ~ntensibr and water temperature, which determines the kinematic viscosity of the water. The designer has no real control over these environmental factors but needs to select appropriate values when analyzing the effect of flow over the pavement surface and hydroplaning potential. Five section types, one for each of the basic geometric configurations used In highway design, are examined. These section are: 1. TaIlgent; 2. Superelevated curve; 3. Transition; 4. Vertical crest curve; and 5. Vertical sag curve. 120

Pavement properties that affect the water fihn thickness mclude surface characteristics, such as mean texture depth and grooving of Portland cement concrete surfaces, are considered In the process of applying PAVDRN. Porous asphalt pavement surfaces can also reduce He water film thickness and thereby contribute to the reduction of hydroplaning tendency and their presence can also be accounted for when using PAVDRN. Finally, PAVDRN also allows the design engineer to consider the effect of drainage appurtenances, such as slotted drain inlets. A complete description of the various elements that are considered In the PAVDRN program is illustrated In figure 40. A more complete description of the design process, the parameters used in the design process, and typical values for the parameters is presented In the "Proposed Design Guidelines for Improving Pavement Surface Drainage" (2) alla in Appendix A. fIN1)INGS The following findings are based on the research accomplished during the project, a survey of the literature, and a state-of-the-art survey of current practice. I. Model. The one~unensional mode} is adequate as a design tool. The simplicity and stability of the one~imensional mode} offsets any increased accuracy afforded by a two-d~mensional model. The one~mensional model as a predictor of water fiDn thickness and How path length was verified by using data from a previous study (11). 121

No. of Planes Length of Plane Grade Step Increment Wdth of Plane Cross Slope Section T,rne 1) Tangent 2) Honzontal Curare 3) Transition 4) Vertical Crest 5) Vertical Sag U=tS 1)U.S. 2) S. I. Rainfall Intenstity ~ , \ |Kinematic Viscosity |Design Speed Note: PC = Point of Curvature PI. = Point of Tangency PCC = Portland cement concrete WAC = Dense graded asphalt concrete 0GAC = 0pcn~raded asphalt concrete where OGAC includes all types of intentally draining asphalt surfaces GPCC = Grooved Ponland cement concrete Taneent Pavement Type Mean Texture Depth 1) PCC 2) DGAC 3) OGAC 4) GPCC Horizontal Cun~c  Grade Cross Slope Radius of Cunran~re Wdth Pavement Type _ 2) DGAC 3) OGAC 4) GPCC Mean Texture Depth Step Increment _ Transition Length of Plane Super Elevation Tangent Cross Slope Tangent Grade width of Curve Transition Width Pavement Type_ 1) PCC 3) OGAC 4) GPCC Mean Texture Depth Step Increment Horizontal Length Cross slope width PC Grade PI' Grade Elevation: Pr-PC Vertical Crest Flow Direction Step Increment Pavement Type 1) PC Side I 2) PI. Side | 1)PCC 2) DGAC 3) OGAC 4) GPCC Mean Tex~rc Depth _ _ ~ Figure 40. Factors considered in PAVDRN program. 122 ~1 r - . , Vertical Sad | Horizontal Length | Cross slope Wldth PC Grade PI Grade Elevation: PIE Flow Direction  Step Increment / Stored :_ ~ cats ~ 1) PC Side | 2) PI Side | . Pavement Typed 1) PCC 3) OGAC 14) GPCC Mean Texture Depth I I

~ Stored data V ~ 3 L IN1T For use with a second nut using data from the first run.) , 1 EPRINT (Echos input to output ) 1 CONVERT (Converts units to and from SI and English.) ~ , ADVP  (Advances Page of output.) KINW (Calculates Minning's n, Water Film Thickness (WEIR), and Hydroplaning Speed UPS).) , EDGE (Determines if flow has reached the edge of the pavement.) out roar Figure 40. Factors considered in PAVDRN program (continued). 123

2. Occurrence of Hydropl~r g. In general, based on the PAVDRN mode! and the assumptions inherent in its development, hydroplaning can be expected at speeds below roadway design speeds if the length of the flow path exceeds two lane widths. 3. Water Film Thickness. Hydroplaning is initiated primarily by the depth of the water film thickness. Therefore, the primary design objective when controlling hydroplaning must be to limit the depth of the water film. 4. Reducing Water Film Thickness. There are no simple means for controlling water John thickness, but a number of methods can effectively reduce water film thickness and consequently hydroplaning potential. These include: Optimizing pavement geometry, especially cross-slope. Providing some means of additional drainage, such as use of grooved surfaces (PCC) or porous mixtures (HMA). Including slotted drains within the roadway. 5. Tests Needed for Design. The design guidelines require an estimate of the surface texture (MTD) and the coefficient of permeability Porous asphalt only). The sand patch is an acceptable test method for measuring surface texture, except for the more open (20-percent air voids) porous asphalt mixes. In these cases, an estimate of the surface texture, based on tabulated data, is sufficient. As an alternative, 124

sand patch measurements can be made on cast replicas of the surface. For the open mixes, the glass beads flow into the voids within the mixture, giving an inaccurate measure of surface texture. Based on the measurements obtained In the laboratory, the coefficient of permeability for the open-graded asphalt concrete does not exhibit a wide range of values, and values of k may be selected for design purposes from tabulated design data (k versus air voids). Given the uncertainty of this property resulting from compaction under traffic and clogging from contaminants and anti-skid material, a direct measurement (e.g., drainage lag permeameter) of k is not warranted. Based on the previous discussion, no new test procedures are needed to adopt the design guidelines developed during this project. 6. Grooving. Grooving of PCC pavements provides a reservoir for surface water and can facilitate the removal of water if the grooves are placed parallel to the flow oath. Parallel orientation is generally not practical because the flow on highway pavements is typically not transverse to the pavement. Thus, the primary contribution offered by grooving is to provide a surface reservoir unless the grooves comlect with drainage at the edge of the pavement. Once the grooves are filled with water, the tops of the grooves are the datum for the Why and do not contribute to the reduction in the hydroplaning potential. 125

7. Porous Pavements. These mixtures can enhance the water removal and Hereby reduce water film tHch~ess. They merit more consideration by highway agencies In the United States, but they are not a panacea for eliminating hydroplaning. As with grooved PCC pavements, the internal voids do not contribute to the reduction of hydroplaning; based on the field tests done In this study. hv~ronImiina can be if, , , ~ expected on these mixtures given sufficient water fiLn thickness. Other than their ability to conduct water through internal flow, the large MTD offered by porous asphalt is the main contribution offered by the mixtures to the reduction of hydroplaning potential. The high-void ~ > 20 percent), modified binder mixes used In Europe merit further evaluation in the United States. They should be used In areas where damage from freezing water and the problems of black ice are not likely. 8. Slotted Drains. These fixtures, when installed between travel lanes, offer perhaps the most effective means of controlling water film thickness from a hydraulics standpoint. They have not been used extensively In the traveled lanes and questions remain unanswered with respect to their installation (especially in rehabilitation situations) and maintenance. The ability to support traffic loads and still maintain surface smoothness has not been demonstrated and they may be susceptible to clogging from roadway debris, ice, or snow. 126

RECOMMENDATIONS AND CONCLUSIONS The following recommendations are offered based on the work accomplished during this project and on the conclusions given previously: I. Implementation. The PAVDRN program and associated guidelines need to be field tested and revised as needed. The program and the guidelines are sufficiently complete so that they can be used in a design office. Some of the parameters and algorithms will I~ely need to be modified as experience is gained with the program. 2. Database of Material Properties. A database of material properties should be gathered to supplement the information contained in PAVDRN. This information should Include typical values for the permeability of porous asphalt and topical values for the surface texture (MTD) for different pavement surfaces to include toned Portland cement concrete surfaces. A series of photographs of typical pavement sections and their associated texture depths should be considered as an addition to the design guide (21. 3. Pavement Geometry. The AASHTO design guidelines (~) should be re-evaluated In terms of current design criteria to determine if they can be modified to enhance drainage without adversely affecting vehicle handling or safety. ~27

4. Use of appurtenances. Slotted drams should be evaluated In the field to determine if they are practical when Installed In the traveled way. Manufacturers should reconsider the design of slotted drains and their Installation recommendations currently In force to maximize them for use In multi-lane pavements and to determine if slotted drains are suitable for installations In the traveled right of way. 5. Porous Asphalt Mixtures. More use should be made of these mixtures, especially the modified high a~r-void mixtures as used In France. Field trials should be conducted to monitor HPS and the long-term effectiveness of these mixtures and to validate the MPS and WDT predicted by PAVDRN. 6. Two-D~mensional Model. Further work should be done with two~mensional models to determine if they improve accuracy of PAVDRN and to determine if they are practical from a computational standpoint. ADDITIONAL STUDIES On the basis of the work done during this study, a number of additional items warrant furler study. These Include: 1. Full-scale skid resistance studies to validate PAVDRN in general and the relationship between water film thickness and hydroplaning potential in particular are needed in light of the unexpectedly low hvdronlanin~ speeds predicted during 128 , . ~. , ~

this study. The effect of water infiltration into pavement cracks and loss of water by splash and spray need to be accounted for In the prediction of water fihn Sickness. Surface Irregularities, especially rutting, need to be considered in the prediction models. 2. Field trials are needed to confirm the effectiveness of alternative asphalt and Portland cement concrete surfaces. These include porous Portland cement concrete surfaces, porous asphalt concrete, and various asphalt m~cro-surfaces. 3. The permeability of porous surface mixtures needs to be confirmed with samples removed from the field, and the practicality of a simplified method for measuring in-situ permeability must be investigated and compared to alternative measurements, such as the outflow meter. 4. For measuring pavement texture, alternatives to the sand patch method should be investigated, especially for use with porous asphalt mixtures. 129

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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21 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

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Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

For qualitative studies, can the findings be structured according to the Research questions? Thank you.

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• Open access
• Published: 02 August 2024

“I didn’t even wonder why I was on the floor” – mixed methods exploration of stroke awareness and help-seeking behaviour at stroke symptom onset

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 , 2 ,
• Christina Stang 1 ,
• Franziska Herzog   ORCID: orcid.org/0000-0002-2504-294X 3 ,
• Melek Sert 1 ,
• Johanna Hoffmann 1 ,
• Jan Purrucker   ORCID: orcid.org/0000-0003-2978-4972 1 ,
• Fatih Seker   ORCID: orcid.org/0000-0001-6072-0438 4 ,
• Martin Bendszus   ORCID: orcid.org/0000-0002-9094-6769 4 ,
• Wolfgang Wick   ORCID: orcid.org/0000-0002-6171-634X 1 ,
• Matthias Ungerer 1   na1 &
• Christoph Gumbinger   ORCID: orcid.org/0000-0002-6137-1169 1   na1  

BMC Health Services Research volume  24 , Article number:  880 ( 2024 ) Cite this article

Metrics details

Introduction

To better target stroke awareness efforts (pre and post first stroke) and thereby decrease the time window for help-seeking, this study aims to assess quantitatively whether stroke awareness is associated with appropriate help-seeking at symptom onset, and to investigate qualitatively why this may (not) be the case.

This study conducted in a German regional stroke network comprises a convergent quantitative-dominant, hypothesis-driven mixed methods design including 462 quantitative patient questionnaires combined with qualitative interviews with 28 patients and seven relatives. Quantitative associations were identified using Pearson’s correlation analysis. Open coding was performed on interview transcripts before the quantitative results were used to further focus qualitative analysis. Joint display analysis was conducted to mix data strands. Cooperation with the Patient Council of the Department of Neurology ensured patient involvement in the study.

Our hypothesis that stroke awareness would be associated with appropriate help-seeking behaviour at stroke symptom onset was partially supported by the quantitative data, i.e. showing associations between some dimensions of stroke awareness and appropriate help-seeking, but not others. For example, knowing stroke symptoms is correlated with recognising one’s own symptoms as stroke ( r  = 0.101; p  = 0.030*; N  = 459) but not with no hesitation before calling help ( r  = 0.003; p  = 0.941; N  = 457). A previous stroke also makes it more likely to recognise one’s own symptoms as stroke ( r  = 0.114; p  = 0.015*; N  = 459), but not to be transported by emergency ambulance ( r  = 0.08; p  = 0.872; N  = 462) or to arrive at the hospital on time ( r  = 0.02; p  = 0.677; N  = 459). Qualitative results showed concordance, discordance or provided potential explanations for quantitative findings. For example, qualitative data showed processes of denial on the part of patients and the important role of relatives in initiating appropriate help-seeking behaviour on patients’ behalf.

Conclusions

Our study provides insights into the complexities of the decision-making process at stroke symptom onset. As our findings suggest processes of denial and inabilities to translate abstract disease knowledge into correct actions, we recommend to address relatives as potential saviours of loved ones, increased use of specific situational examples (e.g. lying on the bathroom floor) and the involvement of patient representatives in the preparation of informational resources and campaigns. Future research should include mixed methods research from one sample and more attention to potential reporting inconsistencies.

Peer Review reports

Acute ischemic stroke is one of the leading causes of death and acquired disability worldwide. Acute treatment options include stroke unit treatment, intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT), all with strongly time-dependent treatment effects. While institutional and regulatory efforts have addressed the time frames from emergency call to treatment initiation [ 1 , 2 , 3 , 4 , 5 ], the time from symptom onset to first help-seeking is largely determined by decisions made by individual medical laypeople. Efforts for raising awareness of stroke are usually based on the assumption that increased stroke awareness will contribute to an increased likelihood of patients behaving correctly, and thereby an increased likelihood of timely treatment access.

However, a positive effect of these efforts has not been shown consistently [ 4 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Moreover, evaluations use a wide range of outcome measures, including knowledge of risk factors, symptoms and treatments [ 9 , 10 , 11 , 12 , 13 , 14 ], action taken [ 9 ], emergency department visits [ 8 ], thrombolysis rates [ 8 ], initiation of reperfusion therapy [ 15 ] or functional outcome at discharge [ 7 ] – all capturing different aspects of how well a person is informed about stroke, knows what to do or actually implements the recommended action. This means that it is not clear to what extent knowledge of stroke symptoms can actually predict good health outcomes, or whether timely presentation to emergency services can really be attributed to higher stroke awareness. Several qualitative studies have pointed out the complexity of the decision-making-process, which in addition to patient-specific factors, is also subject to outside influences [ 16 , 17 , 18 ].

This study aims to (1) assess quantitatively whether different aspects of stroke awareness were associated with appropriate help-seeking behaviour at stroke symptom onset, and to (2) investigate qualitatively why this may (not) have been the case. We expect our results to help inform outreach campaigns and awareness efforts to better reach its target groups and intended goals for improved stroke outcomes.

Mixed methods research design

This study used a convergent quantitative-dominant, hypothesis-driven mixed methods design including patient questionnaires and semi-structured interviews with patients and relatives (Fig.  1 ). The theoretical framework is informed by the COMIC Model, developed for the evaluation of complex care interventions, such as stroke care provision. It focuses on aspects beyond the medical (such as patient-centeredness) and specifically considers the context in which an intervention is implemented, as needed for the current study [ 19 ]. The study was conducted in a German regional stroke network (FAST; www.fast-schlaganfall.de ). Ethics approval was obtained from the Medical Faculty of Heidelberg University (S-306/2016; S-682/2017). All study participants provided written informed consent. We report our findings in line with applicable standards [ 20 ].

The mixed methods integration strategy was to compare (especially regarding patient data) and to expand (especially regarding relative data) [ 21 ]. The mixed methods research data inventory [ 21 ] is shown in Table  1 . We hypothesised that stroke awareness would be associated with appropriate help-seeking behaviour at stroke symptom onset. We defined “stroke awareness” as having information about stroke before the stroke occurred using the concepts “knowing stroke symptoms”, “familiarity with information campaigns”, “having experienced one or more previous strokes” and “knowing other stroke patients” or “having discussed stroke symptoms with other stroke patients”. We defined appropriate help-seeking as responding to a suspected stroke by seeking the appropriate help immediately upon symptom onset, measured using the concepts “recognising the symptoms as stroke”, “no hesitation before calling for help”, “transportation to hospital by emergency ambulance”, and “arrival at hospital within the 4.5 h therapeutic time window”. For mixing of data strands, we conducted a joint display analysis to assess for “fit” and draw meta-inferences according to the categories of concordance, expansion, complementarity or discordance of quantitative and qualitative findings which are addressed in the Discussion [ 22 , 23 ].

Data collection and analysis

Quantitative and qualitative data were collected separately. The quantitative data collection consisted of a questionnaire for patients admitted with acute stroke at an urban university hospital or a rural primary stroke center. Patients were recruited consecutively over a period of 6 months, starting in January 2017. The questionnaires were completed on the day after admission by the patients and their treating physician. Quantitative data were analysed using standard descriptive statistics. Associations were identified using Pearson’s correlation analysis. More detailed information on the quantitative questionnaire is published elsewhere [ 26 ].

For the qualitative data collection, semi-structured interviews were conducted with stroke patients and their relatives. A purposive sampling strategy was used to include interviewees with different stroke pathway experiences such as different transfer modes (helicopter or ambulance), admission at more or less specialised hospitals as well as different health outcomes. Recruitment and data collection place from May to July 2018 at Heidelberg University hospital and from July to September 2019 at two primary stroke centers. Interviews were conducted in German, approximately one month after stroke. The interview guide was piloted in advance with members of a regional stroke self-help group. For qualitative intra-method analysis, interview transcripts were coded by at least two researchers using MaxQDA-software (2018, VERBI, Berlin, Germany). After coding of all transcripts was completed, the quantitative results were used to focus the qualitative analysis on the aspects of stroke awareness and help-seeking behaviour as outlined for the questionnaires.

More detailed information on the respective methods of data collection and intra-method data analysis are shown in Table  2 .

Patient and public involvement

A stroke self-help group consulted on the qualitative design and helped pilot the interviews. Stakeholder validation of preliminary results was conducted with the Patient Council of the Department of Neurology on 17 November 2020, which showed agreement with findings outside the study sample and provided insights into discordance between quantitative and qualitative findings (see Discussion).

Baseline characteristics (questionnaires)

In total, 462 patients were included in the quantitative analysis. Median age was 71.5 years (IQR: 60–79) and 47.4% of patients were female. Median premorbid Rankin scale (pmRS) was 0 (0–2). Other baseline characeristics including primary admission hospital, health status and risk factors are reported in Table  3 .

Summary of main findings

Patient and relative characteristics (interviews)

We conducted 35 interviews, including 28 patient interviews and seven relative interviews. In 8 of the patient interviews, a relative was also present and occasionally participated. The interviews lasted between 20 and 82 min (median: 47 min, IQR: 32–59). Eleven patients were female (39%), and median age was 66 years (IQR: 60–78). Most patients had no prestroke disabilities as indicated by a pmRS of 0 (IQR 0–1). The mean NIHSS at admission was 8.7 (SD 7.7), indicating that most patients had not experienced a severe stroke. The primary admission hospital of eleven patients was an EVT-capable hospital; whereas the others were admitted at an IVT-capable hospital. Mean NIHSS at discharge was 2.6 (SD 2.6) while median mRS at discharge was 2 (IQR 1–3), showing a relatively good outcome after stroke. Of the seven relatives, six were female, and median age was 58, ranging from 23 to 72 years.

Help-seeking and stroke awareness

Main findings are summarised in an integrated visual display in Fig.  2 . This includes statistical results as well as qualitative interview quotes.

Knowing stroke symptoms

Questionnaires showed a positive correlation between knowing stroke symptoms and recognising symptoms as stroke ( N  = 459; r  = 0.101; p  = 0.030*) and arrival at hospital within 4.5 h ( N  = 459; r  = 0.093; p  = 0.046*), but not with no hesitation before calling for help ( N  = 457; r  = 0.003; p  = 0,941) and transportation by emergency ambulance ( N  = 462; r  = 0.014; p  = 0.764).

Five patient interviewees reported immediately knowing or strongly suspecting that they experienced a stroke. One recognized the stroke when he felt a sudden, strong stab of pain in the head and could not hold a water bottle. The other patient recognised the stroke when she saw her drooping cheek in the mirror. Of the five patients who recognized their stroke, four patients immediately called an ambulance or told their spouse to do so. The fifth patient was alone at home and could not physically react appropriately.

In contrast, eight patients who consciously experienced their symptoms stated that they had no idea it was a stroke, e.g. specifying that “[it] was the last thing [he] would have thought of” (Patient, Interview 12). These patients reported slurred speech, not being able to speak or answer questions, not being able to sit/stand/get up or walk (properly), not being able to use their leg(s), lying on the floor, and not being able to use their arm or hand (including dropping things). Another patient specified that even though she was aware of common stroke symptoms, she did not recognise them in her own case.

I know this thing , that you hold up both arms. But for myself , it would never have crossed my mind . Patient , Interview 1

She and another patient emphasised that even though they consciously experienced one or more symptoms, they did not feel that something was wrong.

I thought I had got up to go to the bathroom. I didn’t even wonder why I was on the floor. […] I just felt so comfortably sleepy and thought: Hm , why can’t I get up? Patient , Interview 1

Sometimes patients also initially attributed their symptoms to alternative explanations, i.e. an epileptic attack or hangover. Eight patients were unconscious or too confused to notice their symptoms or did not remember the situation. In these cases, other people called for help on their behalf. Twelve relatives present at symptom onset immediately knew or strongly suspected a stroke based on the symptoms, which included slurred speech, drooping mouth, not being able to speak, paresis, not being able to get up or walk properly, a cramped-up hand, and tingling feelings in one arm.

All relatives suspecting a stroke immediately called for help without waiting for the symptoms to improve or otherwise delaying the process.

I saw that something was wrong with [her] mouth and that’s when I knew it was a stroke . Relative , Interview 6 .

Familiarity with stroke information campaigns

Questionnaires showed a positive correlation between familiarity with stroke information campaigns and recognising symptoms as stroke ( r  = 0.203; p  ≤ 0.001*; N  = 457) but no correlation with no hesitation before calling for help ( r  = 0.009; p  = 0.847; N  = 456), transportation by emergency ambulance ( r  = 0.046; p  = 0.323; N  = 460), and arrival at hospital within 4.5 h ( r  = 0.014; p  = 0.769; N  = 457).

In the interviews, patients were asked about their prior knowledge about the disease stroke and if so, their information sources. Twelve patients indicated that they had had prior information about the disease stroke, naming information sources such as television shows, books and magazines on health topics, knowing other stroke patients, medical conditions because of which they had been told they were at risk for stroke, a previous (own) stroke, and working or volunteering in health care. Of these patients, two patients reported having recognised their stroke, both immediately asking their husbands to call help. Stroke information campaigns were not mentioned by the interviewees.

Many patients who answered “no” to the question “Did you have any prior information about the disease stroke?”, also reported knowing other stroke patients or having discussed their stroke risk or suspected stroke symptoms with a health professional in the months or years before their stroke. Two patients reported actively avoiding information on the topic

When I saw those news articles , I did not read them. […] I skipped them. […] I did not want to know about that. […] I had the feeling […] that I wanted nothing to do with it. Patient , Interview 9 When there was information on TV , I often switched channels. I can’t watch it […] , it upsets me too much. Patient , Interview 34

The latter patient is one of two patients who, despite indicating no prior information about stroke, recognized their stroke at symptom onset. The other patient reported that because of his regular check-up appointments for heart disease he was aware of his stroke risk. The patient was alone at home when the stroke happened but was found by a neighbour who immediately called an ambulance.

Only few patients who indicated having no prior information about stroke also reported not knowing any stroke patients and not having been aware that they were at risk of stroke. In these cases, it was the patient’s partner who initiated help-seeking. In one case, the patient’s wife called an ambulance because of the severity of the symptoms even though she did not realise it was a stroke at the time.

Nine relatives present at symptom onset said they had prior information about stroke, also citing television shows and books on health topics, knowing other stroke patients, the patient’s previous stroke, and volunteering in health care as their main information sources .

Speaking to patient: I saved you. Because I know […]. I do read a lot , and I watch [shows] on TV Relative , Interview 33

All of these relatives recognised the patient’s stroke based on their symptoms and sought help immediately.

Previous stroke

Questionnaire data for having experienced one or more previous strokes showed a positive correlation with recognising symptoms as stroke ( r  = 0.114; p  = 0.015*; N  = 459) but no correlation with no hesitation before calling for help ( r  = 0.027; p  = 0.565; N  = 457), transportation by emergency ambulance ( r  = 0.008; p  = 0.872; N  = 462), and arrival at hospital within 4.5 h ( r  = 0.02; p  = 0.677; N  = 459).

In the qualitative patient sample, four patients had previously experienced a stroke. None of them recognised their second stroke, with two unconscious at symptom onset or unable to recall the situation later. In two cases, patients knew that a stroke had been discovered previously during a routine scan, but they had not been aware of it when it happened (so-called “silent infarctions”). A third patient had experienced his first stroke just a few weeks prior to his second while he was still in rehabilitation for the first. A fourth patient had experienced an acute stroke two years previously. This latter patient did not seem to (want to) realise that this would put him at risk for another stroke:

Interviewer: “Were you aware that having had a previous stroke would put you at risk for another one?” Interviewee: I thought it’s enough now. I […] suppressed it , [put it] out of my mind […]. I thought it would be over now. Patient , Interview 9

In one of the above cases, Patient 9’s wife recognized the stroke and alerted emergency services immediately. In the other cases, no relatives were present and emergency services were instead alerted by unrelated witnesses. A fifth case of a previous stroke was reported by the daughter of a stroke patient who was herself not included in this study. This patient had experienced a severe acute stroke approximately twelve years previously. The daughter reported this as the reason why she recognized her mother’s second stroke and called for help immediately:

She had major speech problems after her first stroke […]. And [this time] I noticed the exact same thing. […] I said: it’s a stroke again. Relative , Interview 24

Knowing other stroke patients

Questionnaires showed no correlation between knowing other stroke patients and recognising symptoms as stroke ( r  = 0.082; p  = 0.081; N  = 455), no hesitation before calling for help ( r  = 0.031; p  = 0.514; N  = 453), transportation by emergency ambulance ( r  = 0.052; p  = 0.264; N  = 458), and arrival at hospital within 4.5 h ( r  = 0.052; p  = 0.272; N  = 455). For those patients who did know other stroke patients and who reported having discussed stroke symptoms with them, a positive correlation was found with recognising symptoms as stroke ( r  = 0.152; p  = 0.026*; N  = 215), and arrival at hospital within 4.5 h ( r  = 0.230; p  = 0.001*; N  = 217) but not with no hesitation before calling for help ( r  = 0.045; p  = 0.506; N  = 216) and transportation by emergency ambulance ( r  = 0.037; p  = 0.588; N  = 217).

In the interviews, thirteen patients reported knowing other stroke patients before, mostly family members and friends, but also colleagues, neighbours and acquaintances. Of these, two patients had recognised their own stroke and called for help immediately. One spoke in detail about her son-in-law’s stroke and thrombectomy treatment as well as the stroke experience of a friend, stating this as the reason “[…] why [she and her husband] had known about stroke since then and also knew about the time window” (Patient , Interview 7) . This was not the case for the other patient who first reported no prior information about stroke before mentioning that his mother had had one at a much older age:

Interviewer: Did you have general prior information about the disease stroke? Interviewee: No. […] Well , [my] mother had a stroke at [88]. Of course , I was aware of that. But , well , riding your motorcycle at [57] , you don’t think about a stroke Patient , Interview 25

A similar pattern was also visibile with other interviewees, who initially responded that they did not know other stroke patients before realising that this was not the case. Nine patients specifically stated that they did not know other stroke patients before their own stroke. Of these, three patients were able to recognise their own stroke, however citing other information sources such as check-ups for heart disease, working in health care, and TV programs.

Seven relatives present at symptom onset reported knowing other stroke patients, with several identifying this as the reason why they recognised their spouse’s stroke and responded appropriately.

We reacted immediately […] because several people in our family already had a stroke , so I know the symptoms. Relative , Interview 29

We explored patients’ and relatives’ help-seeking behaviour at stroke symptom onset using quantitative questionnaires and qualitative interviews. Our hypothesis that having stroke awareness would be positively associated with appropriate help-seeking behaviour was partially supported by quantitative and qualitative data, which confirmed and contradicted each other and sometimes provided potential explanations for apparent inconsistencies, as we discuss below.

Summary and discussion of main findings

Qualitative findings around the impact of knowing stroke symptoms were found to be partially in discordance with quantitative findings. Specifically, questionnaires showed patients with knowledge of stroke symptoms to be more likely to recognise their symptoms as stroke and to arrive at hospital on time. In contrast, interviews showed many patients to not have recognized their symptoms as stroke, even when they knew of common stroke symptoms. Two patients explained that they did not feel ill and even that they felt comfortable. This was confirmed by a former stroke patient in the Patient Council who reported not linking their general knowledge to their acute experience and inexplicably feeling safe and seeing everything through rose-tinted glasses. While the literature shows that lack of pain or perceived symptom severity can contribute to a diminished feeling of urgency, we were not able to find published descriptions of these feelings of comfort or safety [ 16 , 27 , 28 , 29 ].

Regarding the importance of familiarity with information campaigns , our qualitative and quantitative findings complemented each other. While questionnaires showed that patients familiar with campaigns were more likely to recognise their stroke, interviewed patients reported other information sources. Findings from the published literature show a variety of results in terms the impact of stroke information campaigns, e.g. reporting (partial) effectiveness [ 7 , 8 , 10 ] but also rather limited impact [ 6 , 9 ]. Notably, in our study, patient reporting of prior stroke information sometimes appeared inconsistent, e.g. when patients later spoke about a relative with stroke. This suggests that patients have better recall of some types of information than others [ 28 ]. It may also be suggestive of individual patient characteristics contributing to avoidance behaviour. Moloczij et al. called this the desire to “[maintain] a sense of normalcy”, describing several strategies used by patients to support their decision not to take any action, including denial, minimisation of symptoms, and compensating or adapting [ 16 ]. Wang et al. use descriptors such as “hesitating and puzzling” and “doubting – it may only be a minor problem” to describe this process experienced by stroke patients before initiating help-seeking [ 30 ].

Partial discordance was also found for previous strokes . While questionnaires showed patients with one or more previous strokes more likely to recognise their current symptoms as stroke, none of the five patients in the qualitative sample had recognised their current stroke. In their literature review of factors influence prehospital delay and stroke knowledge, Teuschl and Brainin (2010) find that only few studies report shorter time delays or better stroke knowledge in persons having suffered a previous stroke [ 27 ]. While silent (previous) infarctions may explain some of these instances, one patient who actively experienced their previous stroke reported avoidance behaviour before the second stroke. This was also reflected in Mackintosh et al.’s study of why people do (not) immediately contact emergency services, including several patients who recognised their second stroke but did not take action [ 28 ]. This observation was discussed in the Patient Council whose patient representatives showed surprise at the apparent lack of impact of previous stroke experiences. It was discussed whether stroke patients may not perceive themselves as living with a long-term condition requiring ongoing vigilance, but instead an isolated and completed incident.

Finally, qualitative and quantitative data were found to overlap and expand each other for knowing other stroke patients and having discussed the disease stroke . Interviews provided additional insights into possible reasons for when patients did not relate to others’ experiences and showed the importance of relatives knowing other stroke patients. Questionnaires showed no significant associations between knowing other stroke patients and the four dimensions of appropriate help-seeking behaviour, but patients who had discussed symptoms with other stroke patients were found to be more likely to recognise their stroke and to arrive at hospital on time. Again, there appeared to be inconsistencies in the interviews, with patients forgetting and then remembering knowing someone with stroke, and with many patients not relating others’ stroke experiences to their own situation. In contrast, several relatives identified knowing other stroke patients as the specific reason why they recognized the patient’s stroke and knew how to react. The importance of bystander involvement was explored by Mellon et al., identifying symptom recognition and help-seeking by witnesses as critical for a fast response [ 31 ]. For instance, Geffner et al. found that the decision to seek medical help was taken by patients in only 20.4% of cases [ 32 ]. Iverson et al. also found the presence of a bystander at symptom onset to be associated with appropriate help-seeking [ 15 ]. However, other qualitative findings are more nuanced, e.g. with Mc Sharry et al. reporting actions taken by others as having the potential to override patients’ own identification of symptoms and Moloczij et al. finding that sometimes the presence of another person contributed to delayed help-seeking, while at other times facilitating contact with medical services [ 16 , 29 ]. In addition to patients’ and relatives’ own behaviour and decisions, studies also show the importance of system factors, such as inefficient pre-hospital triage for treatment delay [ 33 ].

Strengths and limitations

As data collection was prepared and conducted independently, it was not always perfectly matched. One example of this is the fact that the rural-urban divide was not considered in detail in the qualitative data collection. This means that potentially important qualitative explanations of quantitative findings related to rural vs. urban differences were not explored in the current study, such as potential differences in information access, transport time or time-to-access to emergency services. Moreover, as is appropriate for qualitative interviews, prompting for more detailed information depended on the specific context and was therefore not feasible for all interviewees and all sub-questions. In the questionnaires, patients were asked about prior knowledge of stroke systems after they had their stroke. However, since it was completed on the day itself or day one after treatment, there would not have been much time for extended patient education. Additionally, the quantitative questionnaire was analysed with a pre-defined analysis plan and was collected over a (pre-defined) time period of six months. However, no power or sensitivity analysis was conducted in advance. Finally, our qualitative sample showed very good recovery, which probably affected the range of experiences and reactions covered in the interviews. One might assume that this overrepresentation of good outcomes could suggest a similar overrepresentation of study participants who “acted correctly”. However, given the importance of luck, bystander help, patients’ physical incapability to react and additional factors other than informed decision-making reported in this study, our results indicate that caution is warranted when interpreting good outcomes or arrival inside the time-window as proxies for having acted quickly or correctly (and vice versa). The main strengths of this study are its two-site design covering hospitals in urban and rural areas with differences in acute stroke treatment options, ensuring good external validity for Germany and countries covering larger geographical areas, its mixed methods approach allowing for integration of findings and generation of new perspectives of inquiry, and the involvement of patient representatives in the study preparation and conclusion.

Recommendations

As quantitative and qualitative findings sometimes seemed contradictory, we recommend that future studies collect data from one patient sample (instead of two separate samples, as here), allowing for direct back-and-forth iterations.As qualitative interviews pointed towards relevant inconsistencies in patient reporting, e.g. of prior stroke knowledge even with regard to close family members, it might be worth re-examining the reliability of common quantitative measures of stroke awareness and help-seeking behaviour where these inconsistencies would remain hidden and potentially incorrect. Following the Patient Council discussions, future research may investigate the “comfortable lull” reported by two patients from the study sample and one patient from the Council. If found in more instances, this could contribute to patients not recognizing a situation as highly problematic and requiring urgent action. In terms of practice recommendations, a more family- or community-based approach to stroke information provision may be helpful, emphasising the opportunity to be a loved one’s saviour. This could lessen the impact of avoidance behaviour and increase the positive impact of the presence of a family member on the decision-making process. This may necessitate critical discussions of whether and how relatives should be able to override patient preferences for delayed or no help-seeking behaviour, especially when the patient’s capacity for decision-making is impaired. As many patients seemed unable to apply general knowledge of stroke symptoms in the acute situation, we suggest exploring an example-based approach to risk communication. Specific situational examples (e.g. lying on the floor in the middle of the night or falling down without knowing why) may be a more accessible source of information compared to paresis of the arms or legs. To provide this type of information in the most appropriate way to future patients and their relatives, it seems relevant to involve former stroke patients in the preparation and provision of these informational resources.

Our study provides insights into the complexity of a decision-making process that is influenced by certain factors, but not others – e.g. a previous stroke makes it more likely that a patient recognises their symptoms as stroke, but not that they call for help without hesitation or arrive at the hospital on time. Interviews with patients and relatives provided in-depth insights into these seemingly contradictory findings, e.g. suggesting processes of denial or the inability to translate abstract knowledge into correct actions. We therefore recommend to address relatives as potential saviours of loved ones, increased use of specific situational examples (e.g. lying on the bathroom floor) and the involvement of patient representatives in the preparation of informational resources and campaigns.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. This excludes interview transcripts as ethics requirements to ensure confidentiality do not allow for data sharing outside the research team.

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Acknowledgements

The authors thank all study participants for their participation and valuable contribution to this study. For the publication fee, we acknowledge financial support by Heidelberg University.

There was not external funding for this study.

Open Access funding enabled and organized by Projekt DEAL.

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Matthias Ungerer and Christoph Gumbinger contributed equally to this work.

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Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

Loraine Busetto, Christina Stang, Melek Sert, Johanna Hoffmann, Jan Purrucker, Wolfgang Wick, Matthias Ungerer & Christoph Gumbinger

Institute of Medical Virology, Goethe University Frankfurt, University Hospital, Paul-Ehrlich-Str. 40, 60590, Frankfurt am Main, Germany

Loraine Busetto

Department of Paraplegia, Heidelberg University Hospital, Schlierbacher Landstraße 200a, 69118, Heidelberg, Germany

Franziska Herzog

Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

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LB drafted the manuscript, conceptualised the overall mixed methods study design, and was responsible for the qualitative study design including qualitative data collection and analysis. CS, FH, MS and JH conducted the qualitative interviews and contributed significantly to qualitative data analysis. JP, FS, MB and WW provided medical expertise, contributed to quantitative analysis and revised the manuscript. MU conducted the quantitative analysis and contributed to the mixed methods analysis. CG had a supervisory role, contributed significantly to quantitative analysis and mixed methods design and analysis and relevantly revised different manuscript versions.

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Correspondence to Loraine Busetto .

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Busetto, L., Stang, C., Herzog, F. et al. “I didn’t even wonder why I was on the floor” – mixed methods exploration of stroke awareness and help-seeking behaviour at stroke symptom onset. BMC Health Serv Res 24 , 880 (2024). https://doi.org/10.1186/s12913-024-11276-6

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Challenges with measures used for assessing research impact in higher education institutions

• Andiswa Mfengu Department of Knowledge and Information Stewardship, University of Cape Town, Cape Town, South Africa https://orcid.org/0000-0001-8323-1266
• Jaya Raju Department of Knowledge and Information Stewardship, University of Cape Town, Cape Town, South Africa https://orcid.org/0000-0001-7836-9826

Internationally, there has been a push for the prioritisation of research impact beyond its scholarly contribution. Traditionally, research impact assessments have focused on academic impact and quantitative measures, at the expense of researchers for whom research impact cannot be quantified. Bibliometric indicators and other quantitative measures are still the most widely used method for evaluating research impact because these measures are easy to use and provide a quick solution for evaluators. Conversely, metric indicators fail to capture important dimensions of high-quality research. Hence, in this study, we explored challenges with metric indicators. We adopted a case study of the University of Cape Town and used document analysis, a questionnaire survey to collect data from academics and researchers, as well as semi-structured interviews with a sample of academic and research staff. The findings highlight common challenges with quantitative measures, such as bias and discipline coverage, and the ability of measures to drive researchers’ behaviour in another direction. We propose the adoption of responsible research metrics and assessment in South African higher education institutions for more inclusive and equitable research impact assessments.

Significance:

• The study highlights the importance of understanding the challenges and influence of current measures used for assessing research impact in higher education institutions.
• There is a need for higher education leaders, policymakers and funders to advocate and support responsible metrics.
• Higher education leaders, funders and policymakers need to collaborate at the national level to initiate and support research assessment reform.

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Stress, coping, and adjustment of international students during covid-19: a quantitative study.

1. Introduction

1.1. university adjustment, 1.2. stress, 1.3. ways of coping, 1.4. purpose of this study, 2.1. participants, 2.2. measures, 2.2.1. socio-demographic questionnaire, 2.2.2. student adaptation to college questionnaire (sacq), 2.2.3. perceived stress scale (pss), 2.2.4. ways of coping questionnaire (ways), 2.2.5. covid-19-adjustment questionnaire (covid-19 aq), 2.3. procedures, 3.1. descriptive analyses of variables, 3.2. data analysis, 4. discussion, 5. limitations and future direction, 6. conclusions and implications, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Share and Cite

Wu, Y.; Ding, Y.; Ridgard, T.; Zusho, A.; Hu, X. Stress, Coping, and Adjustment of International Students during COVID-19: A Quantitative Study. Behav. Sci. 2024 , 14 , 663. https://doi.org/10.3390/bs14080663

Wu Y, Ding Y, Ridgard T, Zusho A, Hu X. Stress, Coping, and Adjustment of International Students during COVID-19: A Quantitative Study. Behavioral Sciences . 2024; 14(8):663. https://doi.org/10.3390/bs14080663

Wu, Ying, Yi Ding, Tamique Ridgard, Akane Zusho, and Xiaoyan Hu. 2024. "Stress, Coping, and Adjustment of International Students during COVID-19: A Quantitative Study" Behavioral Sciences 14, no. 8: 663. https://doi.org/10.3390/bs14080663

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Research: quantifying GitHub Copilot’s impact on developer productivity and happiness

When the GitHub Copilot Technical Preview launched just over one year ago, we wanted to know one thing: Is this tool helping developers? The GitHub Next team conducted research using a combination of surveys and experiments, which led us to expected and unexpected answers.

Everyday, we use tools and form habits to achieve more with less. Software development produces such a high number of tools and technologies to make work efficient, to the point of inducing decision fatigue. When we first launched a technical preview of GitHub Copilot in 2021, our hypothesis was that it would improve developer productivity and, in fact, early users shared reports that it did. In the months following its release, we wanted to better understand and measure its effects with quantitative and qualitative research. To do that, we first had to grapple with the question: what does it mean to be productive?

Why is developer productivity so difficult to measure?

When it comes to measuring developer productivity, there is little consensus and there are far more questions than answers. For example:

• What are the “right” productivity metrics? [ 1 , 2 ]
• How valuable are self-reports of productivity? [ 3 ]
• Is the traditional view of productivity—outputs over inputs—a good fit for the complex problem solving and creativity involved in development work? [ 4 ].

In a 2021 study, we found that developers’ own view of productivity has a twist–it’s more akin to having a good day . The ability to stay focused on the task at hand, make meaningful progress, and feel good at the end of a day’s work make a real difference in developers’ satisfaction and productivity.

This isn’t a one-off finding, either. Other academic research shows that these outcomes are important for developers [ 5 ] and that satisfied developers perform better [ 6 , 7 ]. Clearly, there’s more to developer productivity than inputs and outputs.

How do we think about developer productivity at GitHub?

Because AI-assisted development is a relatively new field, as researchers we have little prior research to draw upon. We wanted to measure GitHub Copilot’s effects, but what are they? After early observations and interviews with users, we surveyed more than 2,000 developers to learn at scale about their experience using GitHub Copilot. We designed our research approach with three points in mind:

• Look at productivity holistically. At GitHub we like to think broadly and sustainably about developer productivity and the many factors that influence it. We used the SPACE productivity framework to pick which aspects to investigate.
• Include developers’ first-hand perspective. We conducted multiple rounds of research including qualitative (perceptual) and quantitative (observed) data to assemble the full picture. We wanted to verify: (a) Do users’ actual experiences confirm what we infer from telemetry? (b) Does our qualitative feedback generalize to our large user base?
• Assess GitHub Copilot’s effects in everyday development scenarios. When setting up our studies, we took extra care to recruit professional developers, and to design tests around typical tasks a developer might work through in a given day.

Let’s dig in and see what we found!

Finding 1: Developer productivity goes beyond speed

Through a large-scale survey, we wanted to see if developers using GitHub Copilot see benefits in other areas beyond speeding up tasks. Here’s what stood out:

• Improving developer satisfaction. Between 60–75% of users reported they feel more fulfilled with their job, feel less frustrated when coding, and are able to focus on more satisfying work when using GitHub Copilot. That’s a win for developers feeling good about what they do!
• Conserving mental energy. Developers reported that GitHub Copilot helped them stay in the flow (73%) and preserve mental effort during repetitive tasks (87%). That’s developer happiness right there, since we know from previous research that context switches and interruptions can ruin a developer’s day, and that certain types of work are draining [ 8 , 9 ].

Table: Survey responses measuring dimensions of developer productivity when using GitHub Copilot

Developers see GitHub Copilot as a productivity aid, but there’s more to it than that. One user described the overall experience:

(With Copilot) I have to think less, and when I have to think it’s the fun stuff. It sets off a little spark that makes coding more fun and more efficient.

The takeaway from our qualitative investigation was that letting GitHub Copilot shoulder the boring and repetitive work of development reduced cognitive load . This makes room for developers to enjoy the more meaningful work that requires complex, critical thinking and problem solving, leading to greater happiness and satisfaction.

Finding 2: … but speed is important, too

In the survey, we saw that developers reported they complete tasks faster when using GitHub Copilot, especially repetitive ones. That was an expected finding (GitHub Copilot writes faster than a human, after all), but >90% agreement was still a pleasant surprise. Developers overwhelmingly perceive that GitHub Copilot is helping them complete tasks faster—can we observe and measure that effect in practice? For that we conducted a controlled experiment.

Figure: Summary of the experiment process and results

In the experiment, we measured—on average—how successful each group was in completing the task and how long each group took to finish.

• The group that used GitHub Copilot had a higher rate of completing the task (78%, compared to 70% in the group without Copilot).
• The striking difference was that developers who used GitHub Copilot completed the task significantly faster–55% faster than the developers who didn’t use GitHub Copilot . Specifically, the developers using GitHub Copilot took on average 1 hour and 11 minutes to complete the task, while the developers who didn’t use GitHub Copilot took on average 2 hours and 41 minutes. These results are statistically significant ( P=.0017 ) and the 95% confidence interval for the percentage speed gain is [21%, 89%].

There’s more to uncover! We’re conducting more experiments and a more thorough analysis of the experiment data we already collected—looking into heterogeneous effects, or potential effects on the quality of code—and we are planning further academic publications to share our findings.

What do these findings mean for developers?

We’re here to support developers while they build software—that includes working more efficiently and finding more satisfaction in their work. In our research, we saw that GitHub Copilot supports faster completion times, conserves developers’ mental energy, helps them focus on more satisfying work, and ultimately find more fun in the coding they do.

We’re also hearing that these benefits are becoming material to engineering leaders in companies that ran early trials with GitHub Copilot. When they consider how to keep their engineers healthy and productive, they are thinking through the same lens of holistic developer wellbeing and promoting the use of tools that bring delight.

The engineers’ satisfaction with doing edgy things and us giving them edgy tools is a factor for me. Copilot makes things more exciting.

With the advent of GitHub Copilot, we’re not alone in exploring the impact of AI-powered code completion tools! In the realm of productivity, we recently saw an evaluation with 24 students , and Google’s internal assessment of ML-enhanced code completion . More broadly, the research community is trying to understand GitHub Copilot’s implications in a number of contexts: education , security , labor market , as well as developer practices and behaviors . We are all currently learning by trying GitHub Copilot in a variety of settings. This is an evolving field, and we’re excited for the findings that the research community — including us — will uncover in the months to come.

Acknowledgements

We are very grateful to all the developers who participated in the survey and experiments–we would be in the dark without your input! GitHub Next conducted the experiment in partnership with the Microsoft Office of the Chief Economist, and specifically in collaboration with Sida Peng and Aadharsh Kannan .

• GitHub Copilot

Eirini Kalliamvakou

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Relationships of omega-3 and omega-6 polyunsaturated fatty acids with esophageal diseases: a two-sample Mendelian randomization analysis

Weiming chen.

1 Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China

2 Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China

3 National Key Clinical Specialty of Thoracic Surgery, Fuzhou, China

Maohui Chen

4 Department of Thoracic Surgery Nursing, Fujian Medical University Union Hospital, Fuzhou, China

Qichang Xie

Yizhou huang.

Weifeng Liu, The Second Affiliated Hospital of Soochow University, China

Associated Data

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding author.

Introduction

Omega-3 polyunsaturated fatty acids (PUFAs) have been widely studied and used as nutritional supplements because of their anti-inflammatory effects. Previous studies have shown an association between polyunsaturated fatty acids such as omega-3 and omega-6 PUFAs with the development of malignant tumors. However, the relationships of omega-3 and omega-6 PUFAs with esophageal diseases have not been characterized.

Mendelian randomization (MR) is a statistical method for identifying instrumental variables (IVs) from genome-wide association study (GWAS) data, and is associated with little confounding by environmental or other disease-related factors. We used genome-wide association study (GWAS) data from previously published studies on circulating concentrations of omega-3, omega-6, docosahexaenoic acid (DHA) and linoleic acid (LA), as well as esophageal cancer and other esophageal diseases, which were downloaded from the IEU OpenGwas database ( https://gwas.mrcieu.ac.uk/ ) and the GWAS Catalog database ( https://www.ebi.ac.uk/ ). The inverse variance-weighted approach was used as the principal analysis, and the MR–Egger and weighted median methods were used alongside. A series of sensitivity analyses were used to ensure the robustness of the causality estimates.

We found that the circulating omega-3 PUFAs concentration was positively associated with esophageal cancer ( p = 8 × 10 −4 ), and circulating DHA concentration (the main component of omega-3 in food), was also positively associated with esophageal cancer ( p = 2 × 10 −2 ), but no significant association was found between circulating omega-6 PUFAs and esophageal cancer ( p = 0.17), and circulating LA concentration (the main component of omega-6 in food), was also no significant associated with esophageal cancer ( p = 0.32). We found no significant relationships of circulating omega-3 and omega-6 PUFAs concentration with four other esophageal diseases.

This study indicates that higher levels of circulating omega-3 PUFAs and DHA concentrations may be a risk factor for the development of esophageal cancer. Conversely, an increased omega-6/omega-3 ratio may serve as a protective factor against esophageal cancer. These findings have significant implications for the clinical application of omega-3 PUFAs and the prevention and treatment of esophageal cancer.

1. Introduction

Esophageal cancer is a global health problem, and in a global study of cancer incidence trends, the top three 5 year survival rates for esophageal cancer were in Japan (36%), China (34%), and South Korea (31%), while all other countries had rates of <30% ( 1 ). Esophageal cancer can be associated with Barrett’s esophagus, a history of gastroesophageal reflux disease, obesity, smoking, and alcohol consumption; and its incidence and mortality show substantial regional variations ( 2–4 ). Esophageal cancer is a type of gastrointestinal tumor, and the ingestion of particular foods is thought to be represent a risk factor for esophageal cancer; for example, betel quid chewing and low intake of fresh fruit and vegetables. Furthermore, the lack of certain micronutrients and long-term dietary habits may also represent risk factors ( 4 , 5 ). Omega-3 and omega-6 polyunsaturated fatty acids (PUFAs) are commonly used as dietary supplements and have been demonstrated to exhibit significant prophylactic effects against coronary heart disease and asthma ( 6 , 7 ). Because of their anti-inflammatory properties, omega-3 PUFAs reduce the risk of inflammatory bowel disease ( 8 ); and omega-6 PUFAs may reduce the risks of osteoarthritis of the knee and hip, and therefore may have potential for the prevention and treatment of autoimmune diseases ( 9 ). However, their effects on the risks of tumors are controversial. Previous studies have shown that PUFAs such as omega-3 and omega-6 PUFAs may increase or reduce the risks of developing specific tumors. They have been shown to increase the risks of prostate and endometrial cancers ( 10 , 11 ), but to protect against the development of liver, breast, ovarian, and brain tumors ( 12 ). However, there have been no studies of the relationship of esophageal cancer with omega-3 and omega-6 PUFAs. Mendelian randomization (MR) is an emerging research methodology that is used to determine whether an association exists between particular exposures and outcomes, and it permits the avoidance of the limitations of residual confounding and reverse causation. SNPs are used as instrumental variables to infer whether a relationship exists between exposures and outcomes. The genetic composition of an individual is determined before birth and is therefore not subject to confounding, and Mendelian randomization (MR) studies rely on the fact that genetic variants are randomly assigned during meiosis, such that an unbiased assessment of exposure-outcome relationships can be made ( 13 ). Here, we aimed to characterize the relationships of circulating concentrations of omega-3 and omega-6 PUFA with the development of esophageal cancer.

2. Materials and methods

2.1. study design.

We conducted a two-sample MR study of GWAS data obtained from previously published studies on circulating concentrations of omega-3, omega-6, DHA, LA, as well as esophageal cancer and other esophageal diseases which were downloaded from the IEU OpenGwas database 1 and the GWAS catalog databases. 2 Therefore, the study did not require approval by the institutional ethics committee. To ensure the robustness of the MR data, we made the following three assumptions: (1) genetic variation is associated with specific exposure factors, (2) genetic variation is not associated with confounding factors, and (3) genetic variation affects the outcomes only through specific risk factors. Figure 1 shows the details of the study design.

The flow diagram of MR analysis.

2.2. Data sources

Data regarding the exposures and outcomes were obtained from the IEU OpenGwas database and the GWAS catalog database. The exposure factors were omega-3 PUFAs (ebi-a-GCST90092931), omega-6 PUFAs (ebi-a-GCST90092933), the omega-3/total fatty acid ratio (ebi-a-GCST90092932), the omega-6/total fatty acid ratio (ebi-a-GCST90092935), and the omega-6/omega-3 ratio (ebi-a-GCST90092934), and the data consisted of 115,006 samples and 11,590,399 SNPs ( 14 ). The data regarding omega-3 (met-d-Omega_3) and omega-6 (met-d-Omega_6) PUFAs consisted of 114,999 samples and 12,321,875 SNPs ( 15 ). The data regarding DHA (ebi-a-GCST90092816) and linoleic acid levels (ebi-a-GCST90092880) consisted of 115,006 samples and 11,590,399 SNPs ( 14 ). The outcome factors comprised esophageal cancer (ebi-a-GCST90041891), gastroesophageal reflux disease (ebi-a-GCST90044120), ulcer of the esophagus (ebi-a-GCST90044121), reflux esophagitis (ebi-a-GCST90044122), and Barrett’s esophagus(ebi-a-GCST90044123), and the data consisted of 456,348 samples and 11,842,647 SNPs related to these ( 16 ). Further information regarding the exposure and outcome factors are presented in Tables 1 , ​ ,2, 2 , respectively.

Characters of esophageal disease.

Characters of polyunsaturated fatty acid (PUFA).

2.3. Selection of instrumental variables

We rigorously selected genetic variants that showed close associations with circulating concentrations of omega-3 and omega-6 (genetic correlation: p  < 5 × 10 −8 ) to obtain complete and reliable results. We also further performed quality control using chain disequilibrium ( r 2  < 0.001, 10,000 kb) to ensure that single-nucleotide polymorphisms (SNPs) within a specific window were pruned to assess the bias caused by the residual LD of the genetic variants. The F -statistic represents the closeness of a correlation, and it is generally considered that SNPs with F  > 10 are closely associated with the exposure factors. The formula for calculating the F -statistic and R 2 is as follows ( 17 ). The F -statistics for all of the SNPs included in the study were calculated and found to be >10 and part of the F values were shown in Supplementary Table S1 .

2.4. MR analysis

To determine whether the association of omega-3 and omega-6 PUFA concentrations with esophageal cancer, we used a number of different methods in the two-sample MR analysis. The inverse variance-weighted (IVW) method was used for the primary analysis; this method involves ignoring the intercept in regression and using the inverse of the variance of the outcome for fitting. Therefore, it may be possible to identify a relationship between an exposure and an outcome despite heterogeneity of the IVs, which may yield biased results. Consequently, we used additional methods for the MR analysis (the MR–Egger and weighted median), to overcome the drawbacks of using IVW alone. The biggest difference between the MR–Egger and IVW methods is that the presence of the intercept is taken into account in the regression in the former, but it also uses the inverse of the variance of the outcome for fitting. The weighted median method is able to provide an unbiased estimate of the effects, and therefore it represents a good complementary method of analysis.

2.5. Complementary analysis methods

The central idea of Mendelian randomization is that IVs can only influence the outcome through exposure factors, but if IVs can influence outcomes through an alternative route, there is horizontal multiplicity of results. Therefore, we used MR-PRESSO to conduct a test of pleiotropy, which is also a means of sensitivity testing, as well as two other sensitivity testing methods: the heterogeneity test and the leave-one-out sensitivity test. The heterogeneity test, also known as Cochran’s Q test, is used to determine whether there is heterogeneity among the IVs, while the leave-one-out sensitivity test calculates the MR results of the remaining IVs after removing each IV one by one. It is thus used to investigate the effect of individual IVs on the overall effect.

2.6. Colocalization analysis

We conducted a colocalization analysis to evaluate whether shared SNPs exist with omega-3 and esophageal cancer at common genomic loci. For each SNP associated with omega-3, we performed a colocalization analysis within a 500 kb range upstream and downstream of the genomic region. Our analysis results conform to the following four hypotheses: H0 (the genomic locus is not associated with either trait), H1 (associated with esophageal cancer but not with omega-3), H2 (associated with omega-3 but not with esophageal cancer), H3 (associated with both omega-3 and esophageal cancer through two different SNPs), and H4 (associated with both omega-3 and esophageal cancer through a shared SNP).

3.1. Association of omega-3 and omega-6 PUFAs with esophageal cancer

We performed two sets of MR analyses of the relationships of omega-3 and omega-6 PUFAs with esophageal cancer. In the first set, we chose not to use proxy SNPs, and finally 48 and 56 SNPs were entered into the MR analyses, respectively. We found a positive correlation between circulating omega-3 PUFA concentration and the risk of esophageal cancer, but no significant correlation was found between the circulating omega-6 concentration and the risk of esophageal cancer. The IVW analyses showed close correlations between omega-3 PUFA concentration and esophageal cancer (OR = 2.16, 95% CI = 1.38–3.38, p  = 8 × 10 −4 ), and between omega-6 PUFA concentration and esophageal cancer (OR = 1.51, 95% CI = 0.84–2.72, p  = 0.17). MR-PRESSO analysis did not show evidence of horizontal pleiotropy for the analysis of the relationship between omega-3 PUFAs and esophageal cancer ( p  = 0.83), and the horizontal pleiotropy analysis ( p  = 0.76) generated consistent results. In addition, heterogeneity testing suggested that there was no heterogeneity ( p  = 0.76).

We also obtained data regarding omega-3 and omega-6 PUFA concentrations from another database and performed a second MR analysis as a validation study. For this, we also chose not to use proxy SNPs, and finally included 48 and 55 SNPs, respectively, in the MR analysis. The results of the IVW analysis also showed close relationships of the circulating omega-3 (OR = 2.34, 95% CI = 1.46–3.75, p  = 4 × 10 −4 ) and omega-6 (OR = 1.40, 95% CI = 0.78–2.53, p  = 0.26) PUFA concentrations with esophageal cancer. MR-PRESSO analysis ( p  = 0.35), and horizontal pleiotropy analysis ( p  = 0.28) indicated the absence of horizontal pleiotropy, and heterogeneity analysis suggesting the absence of heterogeneity. The results of the MR analysis are presented in Figure 2 and the results of the complementary analyses are presented in Table 3 .

Preliminary MR analysis of the correlation between omega-3, omega-6 and esophageal cancer, and a forest plot of the three MR analysis methods was drawn.

Supplementary MR result of omega-3, omega-6, and esophageal carcinoma.

3.2. Association of the ratios of omega-3 and omega-6 PUFAs to the total fatty acid concentrations with esophageal cancer

We also conducted an MR analysis of the relationships of the proportions of omega-3 and omega-6 PUFAs of the total fatty acid concentrations with esophageal cancer, and found that the proportion of omega-3 PUFAs in the circulation positively correlated with the risk of esophageal cancer, whereas there was no significant correlation with respect to the proportion of omega-6 PUFAs. The results of the IVW analyses were as follows: omega-3/total fatty acid ratio: OR = 1.71, 95% CI = 1.00–2.92, p  = 0.049; omega-6/total fatty acid ratio: OR = 1.51, 95% CI = 0.84–2.72, p  = 0.17. The results of the MR analysis are presented in Figure 3 and the results of the complementary analyses are presented in Table 3 .

Preliminary MR analysis of the ratio of omega-6 to omega-3, the percentage of both in total fatty acids, docosahexaenoic acid and linoleic acid were plotted as forest plots for the three MR methods in correlation with esophageal cancer.

3.3. Association of DHA and LA with esophageal cancer

Because DHA is the principal omega-3 PUFA, we also performed an MR analysis regarding its relationship with esophageal cancer, and found a positive correlation between its concentration and the risk of esophageal cancer. The results of the IVW analysis were as follows: OR = 1.89, 95% CI = 1.10–3.24, p  = 0.02. The MR-PRESSO analysis did not detect horizontal pleiotropy ( p  = 0.70), and neither did the horizontal pleiotropy analysis ( p  = 0.08). Furthermore, heterogeneity analysis suggested the absence of heterogeneity ( p  = 0.76). LA is the principal omega-6 PUFA, we also performed an MR analysis regarding its relationship with esophageal cancer, and found no association of its concentration and the risk of esophageal cancer. The results of the IVW analysis were as follows: OR = 1.37, 95% CI = 0.74–2.55, p  = 0.32. The MR-PRESSO analysis did not detect horizontal pleiotropy ( p  = 0.43), and neither did the horizontal pleiotropy analysis ( p  = 0.59). Furthermore, heterogeneity analysis suggested the absence of heterogeneity ( p  = 0.47). The results of this MR analysis are presented in Figure 3 and the results of the complementary analyses are presented in Table 3 .

3.4. Association of the circulating concentrations of omega-3 and omega-6 PUFAs with four other esophageal diseases

We found no significant relationships of the circulating concentrations of omega-3 and omega-6 PUFAs with the risk of developing GERD, esophageal ulcer, reflux esophagitis, or Barrett’s esophagus. The result of the IVW analyses for the relationship between omega-3 PUFAs and GERD was: OR = 0.96, 95% CI = 0.91–1.02, p  = 0.18; for that between omega-3 PUFAs and esophageal ulcer was: OR = 1.00, 95% CI = 0.88–1.14, p  = 0.988; for that between omega-3 PUFAs and reflux esophagus was: OR = 0.96, 95% CI = 0.88–1.04, p  = 0.335; for that between omega-3 PUFAs and Barrett’s esophagus was OR = 1.00, 95% CI = 0.93–1.08, p  = 0.967; for that between omega-6 and GERD was: OR = 0.94, 95% CI = 0.87–1.02, p  = 0.14; for that between omega-6 and esophageal ulcer was: OR = 0.978, 95% CI = 0.83–1.15, p  = 0.796; for that between omega-6 and reflux esophagitis was: OR = 0.98, 95% CI = 0.89–1.09, p  = 0.77; and for that between omega-6 and Barrett’s esophagus was: OR = 0.95, 95% CI = 0.86–1.05, p  = 0.328. The results of the MR analyses are presented in Table 4 and the results of the supplementary analyses are presented in Table 5 . Therefore, leave-one-out sensitivity analyses of positive results consistently indicate that each association is not influenced by individual SNPs. The results of the leave-one-out sensitivity analysis are presented in Figure 4 .

MR result of omega-3, omega-6, and esophageal disease.

Supplementary MR result of omega-3, omega-6, and esophageal disease.

Leave-one-out sensitivity analysis, (A) Omega-3 (ebi-a-GCST90092931) and Esophageal Cancer, (B) Omega-3 (met-d-Omega 3) and Exophageal Cancer, (C) Docosahexaenoic acid and Esophageal Cancer, (D) omega-6/omega-3 and Esophageal Cancer, (E) omega-3/total fatty acids and Esophageal Cancer.

3.5. Colocalization analysis of omega-3 and esophageal cancer

We performed a colocalization analysis of omega-3 PUFAs and esophageal cancer. A positive result is defined as H4 > 0.8; however, our analysis showed that H4 was consistently <0.8, indicating no evidence of colocalization. The analysis results are presented in Supplementary Table S1 .

4. Discussion

To the best of our knowledge, this is the first MR study of the relationships of omega-3 and omega-6 PUFAs with esophageal cancer and esophageal diseases. Furthermore, we performed a subanalysis of the relationship between DHA, the principal omega-3 PUFA in food, and esophageal cancer. We found a positive correlation between circulating omega-3 PUFA concentration and esophageal cancer, but no association with other esophageal diseases, and there was no association of the circulating omega-6 PUFA concentration with esophageal cancer or other esophageal diseases, and there was no association of the circulating LA concentration with esophageal cancer there was a positive association between the circulating DHA concentration and esophageal cancer. However, we found a negative association of the circulating omega-6/omega-3 concentration with esophageal cancer. In summary, our study suggests that circulating omega-3 and DHA concentrations may be risk factors for the development of esophageal cancer, whereas an increased omega-6/omega-3 ratio may serve as a protective factor against the incidence of esophageal cancer.

4.1. Status of omega-3 PUFA research in other diseases

Research to date has principally been focused on the relationships of omega-3 PUFAs with chronic diseases such as cardiovascular disease and cancer, and this has primarily involved investigation of the roles of their antioxidant and anti-inflammatory effects in disease onset and progression. It has been suggested that omega-3 PUFAs may be beneficial for patients at risk of a number of diseases and may represent means of both preventing and treating these diseases ( 18 ). A previous meta-analysis showed that supplementation with omega-3 PUFAs alone reduces the risk of cardiovascular events in patients with diabetes, possibly because they modulate the production of a number of anti-inflammatory substances that can promote tissue repair and ameliorate inflammation during atherosclerosis ( 19 , 20 ). This finding was validated in a pooled and harmonized analysis of 29 prospective studies, which showed that supplementation with omega-3 PUFAs is associated with a lower risk of ischemic stroke, probably because omega-3 PUFAs reduce platelet count, reduce arterial stiffness, and improve endothelial function ( 21 ). Although findings regarding the anti-inflammatory effects of omega-3 PUFAs have been inconsistent, a large number of studies have shown that they protect against the development of such disease. However, PUFAs exhibit a wide range of bioactivities at both the molecular and cellular levels, and therefore we should exercise caution regarding their use as nutritional supplements.

4.2. Status of omega-3 PUFA research in tumor

Our research findings demonstrate a positive correlation between circulating omega-3 PUFAs concentrations and the incidence of esophageal cancer, suggesting that elevated circulating omega-3 PUFAs concentrations may be a risk factor for the development of esophageal cancer. In another meta-analysis of data from 67 prospective studies involving 310,955 participants, high omega-3 PUFA concentrations were found to be associated with a lower risk of colorectal cancer ( 6 ). Thus, omega-3 PUFAs may represent a risk factor for, or a means of preventing the development of, malignancy. In one prospective study, the consumption of omega-3 PUFAs in the diet or as supplements was found to increase the risk of endometrial cancer in women with overweight or obesity ( 11 ), and a recent meta-analysis generated consistent findings ( 22 ). Our findings align with similar results obtained in other studies, all of which conclude that omega-3 may act as a potential risk factor for tumor development. Furthermore, a meta-analysis of data from 47 randomized controlled trials showed that increasing long-chain omega-3 PUFA consumption may have little effect on the risk of a diagnosis of cancer or cancer-related mortality, but may slightly increase the risk of prostate cancer ( 10 ). Thus, the inconsistency of previous findings regarding the relationship between omega-3 PUFAs and malignancy are evident and may be related to their many biological activities. However, some prior studies have indicated that omega-3 PUFAs may possess anti-cancer properties or enhance the efficacy of chemotherapy in tumor treatment, serving as a nutritional supplement for disease prevention ( 23 ). A review study discussed the role of omega-3 PUFAs in tumor complications, highlighting their anti-inflammatory and protective effects due to their involvement in the resolution of inflammation. The findings suggested that omega-3 PUFAs and their metabolites might regulate key pathways in cancer-related complications ( 24 ). Furthermore, another review analysis on breast cancer indicated that omega-3 PUFAs supplementation could serve as an adjunct to chemotherapy or other conventional anti-tumor treatments ( 25 ). This may represent a future research direction for polyunsaturated fatty acids such as omega-3 PUFAs. In summary, the current research on omega-3 PUFAs is insufficient, thus warranting further discussion regarding their use as nutritional supplements.

4.3. Status of research on docosahexaenoic acid

The principal omega-3 FAs in food are DHA and EPA, and these have been shown to have similar anti-inflammatory and antioxidant effects to omega-3 PUFAs as a whole in previous studies. In a randomized controlled study, DHA was found to have a superior effSect to EPA on specific markers of inflammation and circulating lipid concentrations ( 26 ). Specifically, DHA caused a larger reduction in the circulating concentrations of IL-8 and triglycerides. However, the anti-inflammatory effects of DHA are not necessarily beneficial, and in a recent secondary analysis of a prospective cohort study, DHA was found to be a potential risk factor for poor appetite during chemotherapy for early breast cancer ( 27 ). These contrasting findings show that future studies of omega-3 PUFAs should be more detailed, and that subgroup studies of specific PUFAs should also be performed.

4.4. Status of research on omega-6 PUFAs

There have also been contradictory findings regarding the roles of omega-6 PUFAs in disease. In a systematic evaluation of data from 19 randomized controlled trials, it was shown that omega-6 PUFAs may have no or little effect on all-cause mortality or cardiovascular events, but that they may reduce the risk of myocardial infarction. However, because of the low quality of the evidence, there is much uncertainty regarding the relationships of omega-6 PUFAs with all-cause mortality and cardiovascular events ( 28 ). There is also uncertainty regarding the relationship between omega-6 PUFAs and the development of malignancy. In one prospective study, they were shown to be positively associated with the development of ER + PR+ breast cancer ( 29 ), but in a meta-analysis, no significant association with cancer risk was identified ( 30 ). Thus, there is a great deal of controversy regarding the relationship between omega-6 PUFAs and the progression of disease, and more high-quality evidence is needed to better evaluate this.

4.5. Status of in vitro testing and omega-6/omega-3 research

It is not easy to perform studies regarding the effects of dietary components on human health; therefore researchers have conducted a large number of in vitro and animal-based studies. In one in vitro study, omega-3 and omega-6 PUFAs were found to increase or inhibit the metastatic potential of gastric cancer via COX-1/PGE3 and COX-2/PGE2, respectively ( 31 ). In an animal study, it was shown that the activation of TLR4 in rats is inhibited by a high dietary omega-6/omega-3 PUFA ratio, which in turn reduces their circulating lipid concentrations, improves their glucose tolerance, and ameliorates their insulin resistance ( 32 ). Our research findings indicate a negative correlation between the ratio of circulating omega-6/omega-3 concentrations and the incidence of esophageal cancer. This suggests that an increased ratio of circulating omega-6/omega-3 concentrations is associated with a reduced risk of developing esophageal cancer. These results are consistent with the conclusions of many previous studies. In a clinical study, the omega-6/omega-3 PUFA ratio was also shown to be associated with health, and in a meta-analysis, it was shown that a high omega-6/omega-3 PUFA ratio reduces the risk of breast cancer ( 33 ). Finally, in another study, it was shown that an appropriate omega-6/omega-3 ratio may help control obesity, as well as playing a key role in disease prevention ( 34 ). Our findings are consistent with those of similar studies. Several studies on omega-6/omega-3 have elucidated that different ratios might yield opposite effects on various diseases, potentially serving a preventive or therapeutic role, but also possibly contributing to disease progression ( 35 , 36 ). Our study employed Mendelian randomization, which is a qualitative research method, and the results indicate only an association, precluding quantitative analysis. Therefore, we currently cannot determine the optimal ratio for the prevention of esophageal cancer. Future research should focus on quantitative studies of omega-6/omega-3 ratios.

4.6. Limitation

There were several limitations to the present analysis. First, most of the data we used were GWAS data relating to European populations, and therefore genetic diversity analyses of other populations are needed to generalize the conclusions. Second, although we used several methods and took rigorous steps to avoid horizontal pleiotropy, genetic variation is extremely complex and we were unable to completely eliminate horizontal pleiotropy. Therefore, studies with larger sample sizes and more advanced methods are required to further validate the results. Finally, because of the limitations of the databases, we did not evaluate the individual relationships of each of the omega-3 PUFAs with esophageal disease.

5. Conclusion

We found that elevated circulating concentrations of omega-3 PUFAs might be a risk factor for the development of esophageal cancer. Conversely, a higher omega-6/omega-3 ratio might serve as a protective factor against esophageal cancer. Currently, the widespread use of omega-3 PUFAs as nutritional supplements warrants further evaluation, and the underlying mechanisms require additional investigation.

Data availability statement

Ethics statement.

Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements.

Author contributions

WC: Conceptualization, Data curation, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. MC: Conceptualization, Data curation, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. JH: Conceptualization, Investigation, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. QX: Data curation, Investigation, Validation, Writing – original draft, Writing – review & editing. YH: Data curation, Investigation, Validation, Writing – original draft, Writing – review & editing. CC: Formal analysis, Funding acquisition, Project administration, Resources, Writing – original draft, Writing – review & editing. YZ: Formal analysis, Funding acquisition, Project administration, Resources, Writing – original draft, Writing – review & editing.

Acknowledgments

We would like to thank all the researchers who have made genome-wide data publicly available. We would also like to thank all of our colleagues who gave their time and labor to this research.

Funding Statement

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Sponsored by Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University; Sponsored by National Key Clinical Specialty of Thoracic Surgery, Fuzhou, China.

1 https://gwas.mrcieu.ac.uk/

2 https://www.ebi.ac.uk/

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2024.1408647/full#supplementary-material

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The use of agricultural extension information services in enhancing maize productivity among smallholder farmers in tana river county, kenya, goudian kilemba gwademba, peter wahome wamae, daniel wambiri muthee, johnson mulongo masinde, abu ahmed adam.

Food security assessment reports have demonstrated that, most food insecure people live in rural areas, with no access to information and technology geared towards enhancing agricultural productivity. This suggests that agricultural information is relevant for agricultural productivity, especially for millions of smallholder farmers, who remain the bedrock for food supply chains in developing countries. This research was carried out with a purpose of evaluating the relationship between the Use of Agricultural Extension Information and Maize Productivity among smallholder farmers in Tana River County. The main objective of the study was to ascertain whether increased use of agricultural extension information correlated with increased maize productivity. The location of the study was Tana River County where a sample of 30 maize farming households was purposely chosen for the study. Data was captured using questionnaires and both qualitative and quantitative data was captured. The research took a correlational study design and through statistical analysis using the Pearson Correlation Coefficient (r) and the simple Linear Regression analysis the relationship between use of extension information services and maize agricultural productivity was evaluated. The research findings revealed that, there was a strong correlation (r=0.7) between use of agricultural extension information services and Maize productivity in Tana River County. The strong relationship was significant in qualifying the research hypothesis. The study also underscored the role of ICT in enhancing the effectiveness of agricultural extension information services and recommended the need to streamline agricultural extension service delivery to ensure seamless access to information. The study was significant in that, its findings are expected to enable agricultural stakeholders appreciate the role of extension information services in enhancing agricultural productivity, besides adding credibility to the agricultural extension information policy agenda for smallholder farmers all over the world.

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An axis of genetic heterogeneity in autism is indexed by age at diagnosis and is associated with varying developmental and mental health profiles

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There is growing recognition that earliest signs of autism need not clearly manifest in the first three years of life. To what extent is this variation in developmental trajectories associated with age at autism diagnosis? Does the genetic profile of autism vary with age at autism diagnosis? Using longitudinal data from four birth cohorts, we demonstrate that two different trajectories of socio-emotional behaviours are associated with age at diagnosis. We further demonstrate that the age at autism diagnosis is partly heritable (h2SNP = 0.12, s.e.m = 0.01), and is associated with two moderately correlated (rg = 0.38, s.e.m = 0.07) autism polygenic factors. One of these factors is associated with earlier diagnosis of autism, lower social and communication abilities in early childhood. The second factor is associated with later autism diagnosis, increased socio-emotional difficulties in adolescence, and has moderate to high positive genetic correlations with Attention-Deficit/Hyperactivity Disorder, mental health conditions, and trauma. Overall, our research identifies an axis of heterogeneity in autism, indexed by age at diagnosis, which partly explains heterogeneity in autism and the profiles of co-occurring neurodevelopmental and mental health profiles. Our findings have important implications for how we conceptualise autism and provide one model to explain some of the diversity within autism.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This research was supported by funding from the Simons Foundation for Autism Research Initiative, the Wellcome Trust (214322\Z\18\Z)，Horizon-Europe R2D2-MH (grant agreement number 101057385), and UKRI (10063472). For the purpose of open access， we have applied a CC BY public copyright licence to any author-accepted manuscript version arising from this submission. S.B.-C. also received funding from the Autism Centre of Excellence at Cambridge, the Templeton World Charitable Fund, the MRC and the National Institute for Health Research Cambridge Biomedical Research Centre. The research was supported by the National Institute for Health Research Applied Research Collaboration East of England. Any views expressed are those of the author(s) and not necessarily those of the funder. Some of the results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no.777394 for the project AIMS-2-TRIALS. This joint undertaking receives support from the European Union's Horizon 2020 research and innovation program and the EFPIA and Autism Speaks, Autistica and the SFARI. The iPSYCH team was supported by grants from the Lundbeck Foundation (R102-A9118,R155-2014-1724 and R248-2017-2003), the NIMH (1R01MH124851-01 to A.D.B.), and EU's Horizon Europe program (R2D2-MH; grant agreement no. 101057385 to A.D.B.). The Danish National Biobank resource was supported by the Novo Nordisk Foundation. High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing, iSEQ, Aarhus University, Denmark (grant to A.D.B.). The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and the authors will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). R2D2-MH has been funded by Horizon Europe [grant agreement no. 101057385], by UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee [grant no.10039383] and by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 22.00277.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

All data we used in the study were from existing datasets. The Cambridge Human Biology Research Ethics Committee gave ethical approval to analyse de-identified data for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

SPARK autism GWAS: https://bitbucket.org/steinlabunc/spark_asd_sumstats/src Finngen autism GWAS: https://www.finngen.fi/en/access_results iPSYCH autism GWAS (unstratified, sex-stratified and age at diagnosis stratified) can be obtained from Anders Borglum and Jakob Grove. Psychiatric GWAS summary stats: https://pgc.unc.edu/ GWAS educational attainment: https://thessgac.com/ GWAS cognitive aptitude: https://cncr.nl/research/summary_statistics/ For ALSPAC, the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool": http://www.bristol.ac.uk/alspac/researchers/our-data/ For MCS, data can be obtain after application through the UK Data Service: https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=2000031 Summary statistics for the SPARK based GWAS will be made upon publication.

https://bitbucket.org/steinlabunc/spark_asd_sumstats/src

https://www.finngen.fi/en/access_results

https://pgc.unc.edu/

https://thessgac.com/

https://cncr.nl/research/summary_statistics/

http://www.bristol.ac.uk/alspac/researchers/our-data/

https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=2000031

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