a research firm conducted a survey to determine

SOLUTION: A research firm conducted a survey to determine the mean amount Americans spend on coffee during a week. They found the distribution of weekly spending followed the normal dist

A research firm conducted a survey to determine the mean amount steady smokers spend on cigarettes d

Question: A research firm conducted a survey to determine the mean amount steady smokers spend on cigarettes during a week. A sample of 49 steady smokers revealed that \(\bar{X}\) = $20 and s = $5.

a. What is the point estimate of the population mean? Explain what it indicates.

b. Using the 95 percent level of confidence, determine the confidence interval for \(\bar{X}\). Explain what it indicates.

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• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

Published on August 20, 2019 by Shona McCombes . Revised on June 22, 2023.

Survey research means collecting information about a group of people by asking them questions and analyzing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyze the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyze the survey results, step 6: write up the survey results, other interesting articles, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research : investigating the experiences and characteristics of different social groups
• Market research : finding out what customers think about products, services, and companies
• Health research : collecting data from patients about symptoms and treatments
• Politics : measuring public opinion about parties and policies
• Psychology : researching personality traits, preferences and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and in longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• US college students
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18-24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalized to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

Several common research biases can arise if your survey is not generalizable, particularly sampling bias and selection bias . The presence of these biases have serious repercussions for the validity of your results.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every college student in the US. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalize to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions. Again, beware of various types of sampling bias as you design your sample, particularly self-selection bias , nonresponse bias , undercoverage bias , and survivorship bias .

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by mail, online or in person, and respondents fill it out themselves.
• An interview , where the researcher asks a set of questions by phone or in person and records the responses.

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by mail is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g. residents of a specific region).
• The response rate is often low, and at risk for biases like self-selection bias .

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyze.
• The anonymity and accessibility of online surveys mean you have less control over who responds, which can lead to biases like self-selection bias .

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping mall or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g. the opinions of a store’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations and is at risk for sampling bias .

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data: the researcher records each response as a category or rating and statistically analyzes the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analyzed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g. yes/no or agree/disagree )
• A scale (e.g. a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g. age categories)
• A list of options with multiple answers possible (e.g. leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analyzed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an “other” field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic. Avoid jargon or industry-specific terminology.

Survey questions are at risk for biases like social desirability bias , the Hawthorne effect , or demand characteristics . It’s critical to use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no indication that you’d prefer a particular answer or emotion.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

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Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by mail, online, or in person.

There are many methods of analyzing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also clean the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organizing them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analyzing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analyzed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyze it. In the results section, you summarize the key results from your analysis.

In the discussion and conclusion , you give your explanations and interpretations of these results, answer your research question, and reflect on the implications and limitations of the research.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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Information security awareness in the insurance sector: cognitive and internal factors and combined recommendations.

1. Introduction

2. literature review, 2.1. information security behavior, 2.2. from information security behavior to awareness, 2.3. information security awareness, 2.4. proposition development & research model, 2.4.1. negative experience, 2.4.2. seta program, 2.4.3. infosec goals, 2.4.4. security complexity, 2.4.5. seta design, 3. materials and methods, 3.1. data analysis, 3.2. reliability & validity, 4.1. descriptive statistics, 4.1.1. malware experience, 4.1.2. phishing experience, 4.1.3. measurement item statistics, 4.2. reliability & validity of results, 4.3. inferential statistics, 4.3.1. proposition results, 4.3.2. additional separate regressions per group, 4.3.3. additional mean comparisons of isa per group, 5. discussion, 5.1. key findings, 5.2. isa and findings in relation to sa, 5.3. research limitations, 5.4. implications for theory, 5.5. implications for practice, 5.6. future research directions, 6. conclusions.

• Research Constructs & Method: Five explaining constructs have been incorporated in the research to answer the research question, and their direct relationship with ISA has been tested by means of weighted regression analyses. The constructs are Negative Experience, SETA Program, InfoSec Goals, Security Complexity, and SETA Design.
• Impact of constructs on ISA: ∘ InfoSec Goals: Have a positive impact on ISA, demonstrating that employees with a strong commitment to information security are more aware. ∘ SETA Program: Has a positive impact on ISA by providing necessary education and training. ∘ Security Complexity: Has a negative impact on ISA, indicating that higher complexity in security measures can decrease awareness and increase cognitive overload.
• Significant Insights: Security Complexity emerged as the most significant factor, followed by InfoSec Goals and SETA Program, aligning with the three levels of ISA within the SA framework.
• Group Analyses: ∘ Additional group analyses and separate regressions have been conducted with the help of two group perspectives, (1) IT and non-IT employees and (2) managers and non-managers. ∘ The separate regressions held a stricter significance level, demonstrating that Security Complexity has a significant contribution for each group. ∘ Mean comparisons did not yield notable findings in ISA per group.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

Appendix D. Convergent & Discriminant Validity

Appendix E. Harman’s Single Factor Test Results

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Djotaroeno, M.; Beulen, E. Information Security Awareness in the Insurance Sector: Cognitive and Internal Factors and Combined Recommendations. Information 2024 , 15 , 505. https://doi.org/10.3390/info15080505

Djotaroeno M, Beulen E. Information Security Awareness in the Insurance Sector: Cognitive and Internal Factors and Combined Recommendations. Information . 2024; 15(8):505. https://doi.org/10.3390/info15080505

Djotaroeno, Morgan, and Erik Beulen. 2024. "Information Security Awareness in the Insurance Sector: Cognitive and Internal Factors and Combined Recommendations" Information 15, no. 8: 505. https://doi.org/10.3390/info15080505

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• Open access
• Published: 20 August 2024

Level of patient contact and Impact of Event scores among Canadian healthcare providers during the COVID-19 pandemic

• Iris Gutmanis 1   na1 ,
• Ayodele Sanni 1   na1 ,
• Allison McGeer 1 , 2 ,
• Robert Maunder 1 , 2 ,
• Nicole Robertson 1 ,
• CCS Working Group &
• Brenda L Coleman 1 , 2  

BMC Health Services Research volume  24 , Article number:  947 ( 2024 ) Cite this article

39 Accesses

Metrics details

Healthcare providers (HCP) continue to provide patient care during the COVID-19 pandemic despite the known risks for transmission. Studies conducted early in the pandemic showed that factors associated with higher levels of distress among HCP included being of younger age, female, in close contact with people with COVID-19, and lower levels of education. The goal of this study was to determine if level of patient contact was associated with concern for post-traumatic stress disorder (PTSD) as measured by the Impact of Event Scale-Revised (IES-R).

This cross-sectional study, embedded within a prospective cohort study, recruited HCP working in hospitals in four Canadian provinces from June 2020 to June 2023. Data were collected at enrolment and annually from baseline surveys with the IES-R scale completed at withdrawal/study completion. Modified Poisson regression was used to determine the association between level of patient contact and concern for PTSD (i.e., IES-R scores ≥24).

The adjusted rate ratio (RR) associated with concern for PTSD among HCP with physical contact/direct patient care was 1.19 (95% confidence interval (CI) 1.03, 1.38) times higher than for HCP with no direct contact. In fully adjusted linear regression models, physical care/contact was associated with higher avoidance and hyperarousal scores, but not intrusion scores.

Conclusions

Administrators and planners need to consider the impact of heightened and ongoing stress among HCP by providing early screening for adverse emotional outcomes and delivery of tailored preventive strategies to ensure immediate and long-term HCP health.

Peer Review reports

Transmission of the SARS-CoV-2 coronavirus has continued since the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic on March 11, 2020 [ 1 ]. Due to the significant physical health impacts of COVID-19, attempts were made to slow viral transmission including quarantine restrictions and preventive methods such as wearing masks, social distancing, closing venues, and vaccination. However, such mitigation strategies, added to the threats of the disease itself, can increase disruption and trauma-related stress [ 2 , 3 ].

Post-traumatic stress disorder (PTSD) is a common outcome after witnessing or experiencing a traumatic event [ 4 ]. In a 2002 Canadian study based on self-reported symptoms, Van Ameringen et al. found that prevalence of PTSD was 2.4% [ 5 ]. In a subsequent self-reported Canadian survey conducted between August and December 2021, the estimated prevalence of PTSD had increased to 5.4% [ 6 ]. Despite ongoing transmission, healthcare providers (HCP) need to provide optimal patient care. During the early pandemic period higher stress levels were reported by HCP secondary to increased workload, constantly changing work environments, provision of care to people with COVID-19, deaths caused by the disease, and fear of infecting themselves and their close contacts [ 7 , 8 ].

Several studies [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ] have used the Impact of Event Scale-Revised (IES-R) [ 16 ] to assess concern for PTSD during the COVID-19 pandemic. Among HCP, younger age, female gender, personal exposure to COVID-19, and lower levels of education have been identified as factors associated with higher levels of concern for PTSD [ 11 , 14 , 17 ]. However, these studies were conducted over short periods and/or early in the pandemic when some transmission mitigation strategies, such as quarantine measures and vaccines, had either not been employed or were not yet available.

This study aims to investigate the level of concern for PTSD, as measured by the IES-R, among Canadian HCP in relation to their level of contact with patients, adjusted for potential confounders, between June 10, 2021 and December 1, 2023. Levels of avoidance, intrusion, and hyperarousal are also explored.

Study design

The COVID-19 Cohort Study was a 3.5 year prospective cohort study following HCP from acute care, rehabilitation, and complex care hospitals in the greater Toronto area (Sinai Health, Sunnybrook Health Sciences Centre, Oak Valley Health (Markham Stouffville Hospital), North York General, Michael Garron Hospital, Unity Health (St. Michael’s Hospital), William Osler Health System, and University Health Network), Hamilton (St. Joseph’s Healthcare Hamilton, Hamilton Health Sciences Centre), Ottawa (The Ottawa Hospital), Alberta (Calgary Health Zone, Grey Nuns Community Hospital, University of Alberta Hospital), Quebec (Centre hospitalier universitaire de Sherbrooke), and Halifax (IWK Health Centre, QE II) as well as private physician or midwifery practices in the Toronto area. Rolling enrollment, from June 2020 to June 2023, occurred following ethical approval at each site.

Consented participants completed annual baseline surveys and illness and vaccination surveys as needed. Participants completed the IES-R once within two weeks of study withdrawal or at study closure (December 1, 2023) (see Fig.  1 ).

Study design and timeline

Participants

Consented HCP were eligible for these analyses if they completed ≥ 50% of their most recent baseline questionnaire; were 18 to 75 years old, inclusive; were employed full- or part-time (> 20 h per week) by a participating hospital; or were a physician, midwife, or nurse practitioner with hospital privileges and who cared for ill patients ≥ 8 h per week. Participants with incomplete IES-R data were excluded from analyses.

The Impact of Event Scale (IES) is a 15-item measure of the frequency with which respondents experience thoughts and behaviours associated with two definitional symptoms of PTSD: intrusion and avoidance [ 18 ]. The scale was revised (IES-R) with the addition of another set of items designed to identify the frequency of hyperarousal symptoms [ 16 ]. The IES-R asks participants to indicate, on a scale from 0 (not at all) to 4 (extremely), how distressing each of 22 listed difficulties have been for them during the previous seven days. To orient participants, the survey was introduced with “You have been working throughout the COVID-19 pandemic”. Overall IES-R scores are the sum of all 22 items (range 0 to 88) and were interpreted using criteria developed by Weiss and Marmar [ 16 ] (0–23: no concern for PTSD; ≥ 24: indicative of concern for PTSD). Subscale scores (avoidance, intrusion, hyperarousal) are the mean of the subscale item scores (range 0 to 4).

In the baseline survey, participants were asked to respond to three questions that asked them to indicate which of five levels of contact they had with inpatients, outpatients, and emergency department patients: 1) not applicable, no close contact with patients; 2) never or rarely in room or confined physical space with patients; 3) in room/confined space with patients, but not within 2 m of their face; 4) in room/confined space and within 2 m of their face, but no physical contact/care; 5) physical contact and/or care of patients. Two metres was selected as a key distance for this study as recommended by the Government of Canada to reduce transmission of COVID-19 [ 19 ]. The highest level of contact in any of the three settings was selected. The four levels of patient contact used in these analyses are: 1) no direct contact; 2) never/rarely in patient rooms; 3) in patient rooms but no contact, and 4) physical care/contact.

Baseline surveys collected demographic and work-related information including age; gender; self-reported health; medications for anxiety, depression, or insomnia; occupation; and current work unit (high-risk: emergency department, adult intensive care unit, or adult inpatient medical unit vs low-risk: all others). COVID-19 vaccine receipt was obtained from the most recent vaccination questionnaire. The study did not use information from the illness/testing surveys.

A mitigation strategy measure that ranked the intensity of non-pharmacological mitigation strategies associated with three sectors (work, education, and other locations) on a four-point scale (0: no restrictions to 3: most stringent restrictions) was included as a study covariate [ 20 ]. The mutually exclusive categories developed by Akanteva et al. are specific to each sector (e.g., category 3: work: all non-essential workplaces closed or operating remotely, only essential services or businesses remain open; education: all schools closed for in-person instruction; other locations: stringent gathering restriction, border closures between provinces for non-essential travel, closure of all indoor activities, and closure of most outdoor activities) [ 20 ]. Periods when restrictions summed to  ≥7  were identified as periods of high mitigation. Periods were determined using Ontario data. While other provinces imposed different restrictions at different times, Ontario was selected as the referent as most study sites were within this province.

Data analysis

Chi-squared or Fisher’s exact tests were used to compare categorical variables while t-tests or median tests were used to compared continuous variables, as appropriate. Modified Poisson regression was used to quantify the relationships between study variables and dichotomized IES-R scores [ 21 ]. All models were adjusted by age and gender. Other study covariates that were not associated with the outcome were sequentially eliminated as per Vittinghoff et al. [ 22 ]. Variance estimates were adjusted in multivariable models for clustering within province. The final model was assessed for goodness-of-fit and multi-collinearity. Linear regression, adjusted for clustering within provinces, was used to assess the association between subscale scores and variables found to be significant predictors of the dichotomized IES-R score.

Participant characteristics

Of the 2648 HCP who participated in the parent study, 1498 (56.6%) submitted an IES-R between June 10, 2021 and December 1, 2023. Those who submitted an IES-R were significantly older, and less likely to be a nurse, work on a high-risk unit, or to have been working for less than five years compared with the full cohort (see Table  1 ).

Almost one third of participants who completed the IES-R were nurses, nurse practitioners, or midwives, 773 (51.6%) provided physical care to patients, and 875 (58.4%) worked in the province of Ontario. The majority of participants were female (1309 or 87.3%) and had a mean age of 42.4 (95% confidence interval (CI) 41.9, 43.0) years. As gender was considered an important study covariate, those who selected “other” were dropped from analyses due to the small sample size.

The top three individual IES-R items most frequently assigned a score of 4, indicating that participants were extremely affected, were trouble staying asleep ( n  = 48/1498 or 3.2%), trouble falling asleep ( n  = 42), and trying not to think about it ( n  = 39). Meanwhile, few ( n  = 6) indicated they were extremely affected by reminders causing physical reactions, such as sweating, trouble breathing, nausea, or a pounding heart.

As shown in Table  2 , median IES-R scores, mean subscale scores, and the percent of respondents with an IES-R score indicative of concern for PTSD were similar in 2021 and 2022 but decreased significantly in 2023 ( p  < 0.001).

Patient contact and concern for PTSD

As seen in Table  3 , the unadjusted rate ratio (RR) associated with concern for PTSD among HCP with physical contact/care was 1.24 (95% CI 1.05 1.47) times higher than for HCP with no direct contact. When confounding variables were added, the adjusted RR associated with physical contact/care decreased by less than 5% to 1.19 (95% CI 1.03, 1.38).

Subscale scores

As shown in Table  2 , the mean subscale scores were 0.87, 0.87, and 0.67 for avoidance, intrusion, and hyperarousal, respectively. As expected, given that subscale items are summed to create the overall IES-R score, the correlation between the IES-R score and each subscale was 0.92 for both avoidance and hyperarousal and 0.96 for intrusion, with no differences in correlations by year of survey completion. There was also high correlation between the subscale scores at 0.88 for intrusion:hyperarousal, 0.79 for intrusion:avoidance, and 0.74 for hyperarousal:avoidance.

In fully adjusted linear regression models, physical care/contact was associated with increased avoidance and hyperarousal scores, but not intrusion scores. Being in the same room as a patient was also associated with higher avoidance scores.

Provision of physical care during the COVID-19 pandemic was associated with increased concern for PTSD among Canadian HCP. Between June 10, 2021 and December 1, 2023, in adjusted regression models, HCP providing physical care to patients had IES-R scores that were higher than for those with no direct patient contact. Patient contact was also associated with increased avoidance and hyperarousal scores but not with not with intrusion scores.

The association between level of patient contact and emotional distress has been found in previous studies. During the 2003 severe acute respiratory syndrome (SARS) outbreak in Toronto, nurses who had longer contact with patients with SARS had higher distress scores [ 24 ]. Similar findings were reported very early in the COVID-19 pandemic (February 2020) in China where working in a frontline position (i.e., directly engaged in clinical activities with patients with elevated temperatures or confirmed to have COVID-19) was associated with significantly higher median IES-R scores (22.5) than those who were not (17.0) [ 13 ].

In this study, in 2023, 22.5% of HCP had scores indicative of concern for PTSD. This rate is significantly lower than the 47.8% with scores of concern in 2021 but is, itself, substantially lower than estimates from other studies conducted with HCP that also used the IES-R with a cut-off score of ≥ 24. In a Canadian study, 74% of critical care nurses had scores of concern [ 25 ] while in Italy, 65% of physicians and 71% of nurses had scores of concern [ 26 ]. However, the estimate from this study is in line with an earlier 2020 Ontario study that also used the IES-R with a cut-off score ≥ 24 that found 50% of HCP had scores suggestive of concern for PTSD [ 27 ].

Fattori et al. [ 28 ] reported that Italian HCP mean IES-R scores decreased from 22 in 2020/2021 to 13 in 2021/2022. Although the scores are similar to the current study, the decrease in scores occurred about a year earlier in the Italian study. In contrast, Th’ng and colleagues reported substantially lower percentages of emergency department HCP who had scores of concern than in our study, at 13.6–16.2% for 2020 through 2022 in their longitudinal single-centre study in Singapore, with no significant change over time [ 29 ].

Studies indicate that HCP have dealt with COVID-19 associated stress in many ways. One in four Canadian HCP reported drinking more in 2021 than before the COVID-19 pandemic [ 30 ] and Canadian adults who screened positive for PTSD were 3.5 times more likely than those with a negative screen to report increased cannabis use since the beginning of the pandemic [ 31 ]. Conversely, 70% of HCP indicated that they are exercising to improve or maintain their health during the pandemic [ 30 ]. Carmassi et al. reported that HCP with IES-R scores of ≥ 24 had significantly higher Work and Social Adjustment Scale [ 32 ] scores than HCP with lower scores [ 33 ], indicating interference in working, recreational, and social activities, household chores, and family relationships. A rapid review of successful strategies to reduce HCP stress indicated that actions need to be taken at the organizational level [ 34 ]. These authors suggest that communication needs to be timely and accurate, and that HCP safety and well-being needs to be kept in the forefront. Healthcare leaders should tailor stress reduction strategies to their population and prioritize HCP who are providing direct patient care.

As with all studies, this study has limitations. Non-random sampling methods were used to generate both the study and the sub-study populations; study participants were self-selected, and some did not complete the IES-R. Biases stemming from differential patterns of study enrollment or attrition could have led to under- or over-estimates of the relationship between level of patient contact and symptoms associated with concern for PTSD. It is unknown if we over- or under-sampled HCP with PTSD symptoms (% of participants with symptoms indicative of concern for PTSD; current study 2021: 47.8%; 2020 Honarmand et al. [ 27 ]: 50%; 2021 Crowe et al. [ 25 ]: 74%; 2021 Gorini et al.: 65% (physicians) [ 26 ]). In addition, the overall variance explained by all regression models was low which is likely due to unmeasured known (e.g., pre-existing mental illness [ 35 ], fear of COVID-19 [ 36 ], personality [ 37 ]), and unknown covariates/confounders/mediators. As well, all results are self-reported and may suffer from social desirability bias. Although we have no data from before (nor early) in the pandemic, one strength of the study is its longitudinal nature, with data from 2021 through 2023. Another strength is that participants were from four provinces across Canada, increasing generalizability.

HCP who provided direct patient care were significantly more likely to have IES-R scores indicative of concern for PTSD than those reporting no direct patient contact. Healthcare system administrators and planners must consider the high prevalence of concern for PTSD among HCP who worked during the COVID-19 pandemic when reflecting on current human health resources. Early screening for adverse emotional outcomes during stressful times followed by the delivery of timely, tailored preventive strategies is vital for both immediate and long-term HCP health.

Availability of data and materials

Study data cannot be shared openly. The datasets generated and/or analyzed during the current study are not publicly available due to information that could compromise the privacy of research participants. Data are available from the corresponding author on reasonable request.

Abbreviations

Post-traumatic stress disorder

• Healthcare provider

Impact of Events Scale

Impact of Events Scale-Revised

Severe acute respiratory syndrome

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Acknowledgements

The investigators thank their staff, who worked tirelessly throughout the studies and the participants, who gave freely of their time throughout this stressful pandemic.

CCS Working Group

Curtis Cooper, University of Ottawa, 75 Laurier Ave E, Ottawa, ON Canada K1N 6N5.

Kevin Katz, North York General Hospital, 4001 Leslie St, Toronto, ON Canada M2K 1E1

Mark Loeb, Hamilton Health Sciences Centre, 237 Barton St East, Hamilton, ON Canada L8L 2X2

Shelly McNeil, Dalhousie University, 5820 University Ave, Halifax, NS Canada B3H 2Y9

Matthew Muller, Unity Health Toronto, 30 Bond Street, Toronto, ON Canada M5B 1W8

Jeff Powis, Michael Garron Hospital, 825 Coxwell Avenue Toronto, ON Canada M4C 3E7

Robyn Harrison, Division of Infectious Diseases, University of Alberta, 8440 112 St, Edmonton, AB Canada T5J 3E4

Joanne Langley, Canadian Center for Vaccinology, 5850 University Ave, Halifax NS Canada B3K 6R8

Samira Mubareka, Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON Canada M4N 3M5

Jeya Nadarajah, Oak Valley Health, 381 Church St, Markham, ON Canada L3P 13P

Louis Valiquette, hospitalier universitaire de Sherbrooke, 2500 Bd de l’université, Sherbrooke, QC Canada J1K 2R1

Marek Smieja, St. Joseph’s Healthcare, 50 Charlton Ave East, Hamilton, ON Canada L8N 4A6

This work was funded by the Canadian Institutes of Health Research [173212 & 181116]; Physician Services Incorporated Foundation [6014200738]; and the Weston Family Foundation [no number]. Funders had no role in the collection, analysis, or interpretation of the data, writing of the manuscript, nor the decision to submit it for publication.

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Iris Gutmanis and Ayodele Sanni contributed equally to this work as co-first authors.

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Iris Gutmanis, Ayodele Sanni, Allison McGeer, Robert Maunder, Nicole Robertson & Brenda L Coleman

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The authors confirm contribution to the paper as follows: conceptualization: BLC, RM, AMdata curation: BLC, IG, NRformal analysis: BLC, NRfunding acquisition: BLC, AM, CCS Working Groupmethods: BLCproject administration: BLCresources: BLC, AM supervision: BLCvalidation: BLC, ASoriginal draft: AS, IGreview & editing: BLC, IG, AS, RM, AM, NRApproval of submitted version: IG, BLC, AS, NR, RM, AM, CCS Working GroupAgree to be personally accountable for own contributions and to ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated, resolved, and the resolution documented in the literature: IG, BLC, AS, NR, RM, AM, CCS Working Group

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The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Research Ethics Boards of Sinai Health System (20–0080-E, 2020–04-17), Sunnybrook Health Sciences Centre (1644, 2020–04-13), Michael Garron Hospital (807–2004-Inf-055, 2020–04-29), North York General Hospital (20–0017, 2020–05-06), University Health Network (20–5368, 2020–05-21), Unity Health Toronto (20–109, 2020–06-01), Oak Valley Health (121–2010, 2020–11-04), William Osler Health System (2020–12-18), Hamilton Health Sciences Centre (12809, 2020–12-31), St. Joseph’s Healthcare Hamilton (13044, 2020–12-31), University of Alberta (Pro00106776, 2021–01-13), Nova Scotia Health (1026317, 2021–02-02), The Ottawa Hospital (20210024-01H, 2021–02-05), and Centre hospitalier universitaire de Sherbrooke (MP-31–2021-4104, 2021–06-09).

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Gutmanis, I., Sanni, A., McGeer, A. et al. Level of patient contact and Impact of Event scores among Canadian healthcare providers during the COVID-19 pandemic. BMC Health Serv Res 24 , 947 (2024). https://doi.org/10.1186/s12913-024-11426-w

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