sample research paper on mental health

Mental Health Research Paper

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I. Introduction

Academic writing, editing, proofreading, and problem solving services, get 10% off with 24start discount code, ii. the sociology of mental health: a brief history, a. the development of social epidemiology of mental health and disorders, iii. the study of mental health in contemporary sociology, a. the influence of other disciplines on the sociology of mental health, b. theoretical perspectives on mental health and disorder in sociology, c. defining a unique sociological approach to mental health and illness, 1. the stressor exposure perspective, 2. the social relationships perspective, 3. the societal reaction perspective, d. the influence of psychological models on the sociology of mental health and illness, e. methodological controversies, 1. measures of mental health and disorder, 2. measures of stressor exposure, f. the social epidemiology of mental disorders, 2. socioeconomic status, 4. marital status, iv. future directions in the sociology of mental health, a. comorbidity, b. mental health services and policy, c. better measures of stress exposure, d. better measures of social resources, e. the biological perspective on mental disorders, more mental health research papers:.

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This research paper describes the history, application, and development in sociology of the study of mental health, illness, and disorders. Mental health, mental illness, social and mental functioning, and its social indicators are a classic theme in the field of sociology. Emile Durkheim’s (1951) Suicide was a landmark study in both sociology and epidemiology, laying out a sociological course of research that remains an intellectual force in contemporary social science (Berkman and Glass 2000). The influence of the sociology of mental health and illness goes well beyond its sociological roots; its major theoretical perspectives interact with major research streams in psychiatry, psychology, anthropology, public health, and medicine (Aneshensel and Phelan 1999; Horwitz and Scheid 1999; Eaton 2001; Gallagher 2002; Cockerham 2005). The sociology of mental health also connects to numerous other fields in sociology, including general medical sociology, the sociology of aging, demography and biodemograpy, statistics, childhood studies, sociology of the life course, deviance, criminology, stratification, and studies of the quality of life.

Mental health, mental illness, and mental disorder are closely related but distinguishable concepts. Mental health refers to a state of well-being or alternatively, a state of mental normality, free of disorder or illness. Mental illness refers to a persistent state of mental abnormality. The term mental disorder is applied to a specific diagnosis of mental abnormality, such as depression, anxiety, schizophrenia, agoraphobia, mania or substance dependence.

In this research paper, the term sociology of mental health is used to refer to general theories and research that encompass the causes, development, and consequences of mental disorders and the state or symptoms of mental distress. The term also includes the study of personal and situational resources that preserve or restore the state of mental wellbeing. Sociologists who practice in the field of mental health examine a variety of outcomes and indicators of mental health as well as mental disorders.

The paper is organized into three sections: (1) a brief historical perspective on the study of mental health and illness in sociology; (2) the current state of research in the field, including its major themes and methodological problems; and (3) the future directions of the field. This research paper has four pervasive themes: (1) the interaction of the sociology of mental health and disorder with psychology, psychiatry, public health, and medicine; (2) the environmental perspective, which is the major contribution of the sociology to the mix of disciplines examining mental health in society; (3) the relationship between the study of mental health and studies of mental disorder; and (4) the emergence of the life course perspective as a dominant theoretical perspective in the sociology of mental health.

The topic of mental health has a venerable tradition in sociology. Durkheim’s classic work Suicide was translated into English in 1921, and it is still widely cited in the field. Durkheim’s work encouraged interest in the relationship of mental health and disorders with social structure, group membership, geographical location, and other indicators of social integration and organization. One of the most famous early applications of Durkheim’s perspective was Robert Merton’s (1938) work on social structure and anomie. Taken together, Durkheim and Merton introduced the influential idea that social systems can produce “stress” for individuals, who in turn may act in deviant or disordered ways (Cockerham 2005). Also applying Durkheim’s ideas, Faris and Dunham (1939) conducted a study of the distribution of schizophrenia in Chicago. Observing that people with schizophrenia clustered in high poverty areas, they argued that social isolation encouraged the development of symptoms characterizing schizophrenia.

Although Merton’s and Faris and Dunham’s theories no longer hold sway among contemporary sociologists of mental health, they are significant in their historical impact on the field. The organized field of the sociology of mental health grew out of the larger field of general medical sociology in the late 1930s and 1940s. Interest in mental illness and its causes were heightened by extraordinary events in the mid-twentieth century. The suffering of many ordinary Americans during the Great Depression, the discovery of psychiatric impairments among many World War II draftees, and the traumatic effects of combat on soldiers and civilians were powerful arguments for government support of efforts to mitigate mental illness (Kirk 1999).

The founding of the National Institutes of Mental Health (NIMH) in 1949 contributed to the development of medical sociology in general. The establishment of the Laboratory of Socio-Environmental Studies at NIMH in 1952 was a critical event in the development of studies of mental health in medical sociology. The sociologist John Clausen, who headed the laboratory, recruited and supported a number of sociologists who became leaders in the field, among them Melvin Kohn, Leonard Pearlin, Erving Goffman, and Morris Rosenberg (Kirk 1999). Using a strategy still dominant in behavioral science approaches to mental disorders, Clausen (1956) recruited social scientists from multiple disciplines as well as sociologists, stating that “the roles to be filled by sociologists within the mental health field call for collaboration with clinicians” (p. 47).

Throughout the 1950s, 1960s, and 1970s, NIMH was a major supporter of sociological and psychological research on mental health and illness. According to figures assembled by Kirk (1999), in 1976 more than 50 percent of NIMH research grants were to social, psychological, and behavioral scientists. A smaller proportion of grants were awarded to psychiatrists and physicians (a situation that no longer holds at NIMH).

Social epidemiology, sometimes labeled psychiatric epidemiology or social psychiatry (Gallagher 2002), is the discovery and documentation of the social and demographic distribution of mental disorders and health. The distribution of mental disorders can be documented via the study of medical records, mental hospital admissions, and surveys of the general population. Surveys in representative community populations, using clinically validated questions that identify and classify mental disorder symptoms by diagnostic categories, are the current tools used to estimate the prevalence of disorders (Cockerham 2005). The diagnostic estimates are then analyzed to determine their distribution by social and demographic group.

Hollingshead and Redlich (1958) (a sociologist and a psychiatrist) conducted an innovative study of mental disorders in New Haven, Connecticut, in which they compared mental illness inpatients and outpatients to a sample representative of the general community. Although not a study of prevalence the study had wide influence because of their findings that different types of mental disorder were distributed by social class, with more disorders among lower social class groups. The study also found that treatment for mental disorder varied by class. Because Hollingshead and Redlich’s study included only treated cases, however, they could not draw inferences about possible social causes of mental disorders.

The Midtown Manhattan Study in the 1950s (Srole et al. 1962) investigated the distribution of mental disorders using a random selection household survey design. The interview responses were rated by psychiatrists on the team. The findings from this study continue to shape social epidemiology today. Mental disorders were found to be more prevalent among respondents of lower socioeconomic status. Childhood poverty was linked to psychiatric impairment in adulthood (an early application of the life course perspective on mental health). Those who had mental disorders were less likely to be upwardly mobile. The investigators hypothesized that exposure to childhood and adult stressors played a key role in the distribution of mental disorders as well as mental health (Cockerham 2005). Many of these findings were replicated in a study of Nova Scotia communities (Leighton et al. 1963).

The environmental perspective on mental health was also advanced by studies led by social psychologists. Americans View Their Mental Health, two nationally representative interview studies conducted in 1956 and 1976 (Veroff, Douvan, and Kulka 1981), examined patterns over time in the contributions of the social environment to both positive and negative mental well-being as well as to patterns of help seeking for those who experienced mental distress.

A notable advance in the survey technology for measuring the prevalence of mental disorders and their social correlates was the Epidemiological Catchment Area (ECA) project, conducted by NIMH and five universities in the 1980s (Yale University, Johns Hopkins University, Washington University, Duke University, and the University of California at Los Angeles). A multidisciplinary team, including sociologists, psychiatrists, and psychologists developed new diagnosis instruments to detect mental disorders for use in the general population (Robins and Regier 1991). These diagnostic instruments, derived from the third version of the Diagnostic and Statistical Manual of the American Psychiatric Association (DSMIII), were coupled with interviews that measured environmental factors, social class, race, ethnicity, stressors, social relationships, and other factors believed to correlate with the risk of developing mental disorders.

The separate samples for the ECA studies, however, were not representative of the entire population of the United States. In 1990 through 1992, NIMH funded the first national survey of mental disorders in the general U.S. population (n = 8,068), the National Comorbidity Survey (NCS; Kessler and Zhao 1999). The investigators updated the interview diagnostic measures to reflect those recently developed by the American Psychiatric Association and the World Health Organization (Kessler et al. 1994). Along with diagnostic measures of depression, mania, anxiety, substance abuse, phobias, posttraumatic stress disorder, and other mood and psychotic disorders, the NCS interviews included measures of environmental factors, personality, childhood conditions, physical health, and mental health care utilization. NCS investigated the concept of comorbidity, which is defined as the occurrence of more than one type of mental disorder in an individual.

The NCS has been widely emulated and expanded. A version of the NCS was also conducted in Canada. NIMH also funded a series of replications of the NCS in 2000 to 2003 (Kessler et al. 2005), and the method has been extended to studying mental health and illness in children. The World Health Association is currently coordinating international replications of the NCS ( http://www.hcp.med.harvard.edu/ncs/ ).

As the foregoing brief historical overview shows, the study of mental health in sociology has been influenced by multiple disciplines. It is also host to a number of competing theoretical perspectives. The most widely discussed is the tension among medical, environmental, and societal reaction perspectives on the causes, consequences, and appropriate treatment of mental disorders. As a consequence of the host of influences on the field, there is considerable disagreement over the measurement of basic concepts in research, including how to define mental health and disorders (Kessler and Zhao 1999), environmental factors such as stressors, location, and socioeconomic status (Wheaton 1999); and social consequences such as disability, labeling, and social isolation (Horwitz and Scheid 1999; Pillemer et al. 2000). In addition, there is considerable creative tension between those who concentrate on establishing the incidence and prevalence of mental disorders and those who focus more on the correlates of mental health and mental illness (Mirowsky and Ross 2002, 2004). Finally, there is considerable research on the use of mental health services and on mental health policy.

As Clausen (1956) prophetically foresaw, sociologists who specialize in mental health frequently collaborate with those in other disciplines, such as developmental and social psychology, psychiatry, epidemiology, economics (Aneshensel and Phelan 1999; Gallagher 2002), and increasingly biology (Shanahan and Hofer 2005). The National Institutes of Health has encouraged and continues to encourage multidisciplinary approaches to the study of mental illness and disorders. Psychiatrists and clinical psychologists lay claim to the definitions of mental illness and disorder through the continuing revisions of the Diagnostic and Statistical Manual Mental Disorders, currently in its fourth edition (American Psychiatric Association 2000), as well as to measurements of mental distress (Radloff 1977), quality of life (Veroff et al. 1981), and social relationships and support (Cohen, Underwood, and Gottlieb 2000). Sociologists who study mental health compete for federal funds and intellectual prestige with those from other disciplines.

The presence of sociologists in interdisciplinary efforts to understand the causes, course, and consequences of mental illness and disorders is a positive situation; the influence of the sociology of mental health on other disciplines is tangible. A negative aspect of the interdisciplinary effort is that the sociology of mental health is sometimes viewed as isolated from the general field of sociology (Aneshensel and Phelan 1999). This perception may be exacerbated by the employment of sociologists of mental health (and other medical sociologists) in academic units other than Sociology departments. Members of the Sociology of Mental Health section of the American Sociological Association are employed in medical schools, schools of public health, schools of social work, and departments of human development. When theories of cause and measures of critical outcomes are shared with other disciplines, the question arises: What is the unique contribution of sociology to the study of mental health and illness? The answer to this question is pressing as there are calls for proposals that contribute to “the development, enhancement, and assembly of new data sets from existing data” and for research “that combines diverse levels of analysis” from national research and review bodies (National Institutes of Health 2004) as well as for research that examines the causes of health differences by socioeconomic status and behavioral risk factors across the life course (National Research Council 2004).

Five major perspectives, and combinations of these perspectives, are used in the contemporary sociology of mental health. The five major perspectives are (1) the medical model, (2) the environmental perspective, (3) the social psychological perspective, (4) societal reaction (or labeling), and (5) the life course perspective. The medical model views mental disorders as diseases and prescribes medical treatment as the appropriate cure. The environmental perspective asserts that factors such as social class, race, ethnicity, gender, urban location, and exposure to stressors may cause and most certainly shape risks for mental disorder. The social psychological perspective contributes insight into the social and relational factors that provide resources for adjusting to environmental stressors and restoring mental health and well-being. The social reaction perspective argues that mental illness emerges from social strain processes that produce deviance. The life course perspective views mental health and mental disorder as resulting from the accumulation of environmental stressors and exposures across the lifetime, in interaction with developmental and personal factors such as family structure, personality, and even genetic endowment. Researchers in the sociology of mental health often combine one or more of these perspectives in their research, with the life course perspective now generally seen as an emerging unifying paradigm (George 1999).

Although there is constant interaction between the mental health disciplines, several recent analyses of the state of theory in the sociology of mental health in the late twentieth century indicate the emergence of a distinct sociological approach. Horwitz and Scheid (1999) outlined two major approaches in the study of the sociology of mental health and illness. These two approaches are: (1) the social contexts producing or shaping mental health and disorder and (2) the recognition, treatment, and policy response to mental illness and disorder. In the same volume, Thoits (1999) described three major approaches that uniquely characterize the sociology of mental health: (1) stress exposure (a subset of the social context approach described by Horwitz and Scheid); (2) structural strain theory, which derives from Merton (1938); and (3) societal reaction, or labeling theory. Aneshensel and Phelan (1999) argue that the distinguishing issue in the sociological approach to mental illness is attention to how social stratification produces the unequal distribution of both disorders and mental health.

Aneshensel and Phelan also argue that a major challenge to the sociological approach to mental disorders is the debate between social causation and social selection explanations for the relationship between mental disorders and social class. The social selection approach hypothesizes that the reason there are more mental disorders in the lower economic class is because those with mental disorders are downwardly mobile economically or are unable to be upwardly mobile. This debate has many implications for interpreting how social stratification is linked to mental disorders and health (e.g., Miech et al. 1999).

The sociological approach also provides unique insight into the serious social consequences for those who have mental disorders, including socioeconomic success. The sociological approach also contributes research on the social factors that influence how institutions and individuals recognize when someone is mentally ill, how individuals are treated and how that treatment varies by social class, gender, and race, and who is more likely to use mental health care (e.g., Phelan et al. 2000).

The application of the sociological approach to mental health generates considerable empirical work that focuses on economic and other types of social stratification as determinants of mental health and mental disorder. This work is concentrated in research on stressor exposure, social relationships, and societal reaction to mental disorders.

The social context approach is a set of perspectives; the most well-known and applied outside the field of the sociology of mental health is the stress exposure perspective, which assumes that a combination or accumulation of stressors and difficulties can cause an onset of mental disorder. This perspective (Brown and Harris 1978; Dohrenwend et al. 1978), dominant in sociology, focuses on the level of change or threat posed by external events, and more recently, on the potential for chronic, unresolved stressors to threaten physical and mental health (Wheaton 1999).

Building on the strong history of social epidemiology in the field, the major assumption of this approach is that differential exposure to stressors by social class or social location is largely determined by social inequalities. In turn, the effects of prolonged stress exposure may perpetuate social inequality through the development of mental illness or disorder in disadvantaged populations (Pearlin et al. 2005). The latter point is more controversial (and in general less well developed theoretically); however the emerging life course or human developmental approach to the accumulation of disadvantage derives in some part from the stress exposure perspective (George 1999). The life course approach assumes that there is an accumulation of the negative effects of differential stressor exposure across life that perpetuates and magnifies inequalities and that many of these processes originate in childhood (e.g., McLeod and Kaiser 2004; McLeod and Nonnemaker 2000). A related stress exposure approach is stress diathesis, which assumes that stress exposure causes disorder only when there is a latent vulnerability (Eaton 2001). The diathesis approach is widely applied in psychiatric research on mental disorders.

Horwitz and Scheid (1999) add that in addition to stressor exposure, resources to help counter the negative impact of stressor exposure or to avoid stressor exposure also are differentially distributed by social class and location. The major types of social resources that vary by social class are (1) social integration, usually measured as access to meaningful and productive social roles (e.g., Pillemer et al. 2000); (2) social network characteristics (Turner and Turner 1999); (3) family structure (e.g., Turner, Sorenson, and Turner 2000); (4) received and perceived social support (Wethington and Kessler 1986); and (5) coping choices and styles (Pearlin and Schooler 1978; Pearlin et al. 1981). Thoits (1999) has pointed out that this approach, although distinct from the stressor exposure perspective, relies on stress exposure as a mechanism to activate the protective factors.

In an overview of the sociology of mental health, Thoits (1999) argued that there is no strong evidence that labeling or other societal reaction processes produce mental illness. However, the societal reaction perspective does provide an insight into social biases against those who display symptoms of mental disorder, which are often viewed as socially deviant. Aneshensel and Phelan (1999) concluded that there is a consensus among sociologists of mental health that mental disorders are objective entities and are not completely a product of social constructions. The strongest evidence for this conclusion is that symptoms of mental disorders are observed in all societies, although there are cultural variations in the ways that such symptoms are described and diagnosed.

A difficulty with this position for sociologists of mental health is that it implies there is widespread acceptance of the medical model, which can make theoretical interaction with other streams of sociology (e.g., the sociology of deviance) more contentious. Studies of the etiology of mental disorders in the population no longer routinely employ a deviance perspective. The stressor exposure model also applies a variation of the dose-response paradigm widely used in medical research. This acceptance of a variation of the medical model remains controversial and is probably related to the distance perceived between the sociology of mental health and the more mainstream sociology of stratification.

Yet another tension exists between opposing explanations of what causes social stratification in the distributions of mental disorders. On one side is the belief that routine functioning of society produces some of this stratification, as for example gender differences in the distribution of different types of disorders (Rosenfield 1999). In this view, mental distress and mental disorders can be produced by normal social processes such as gender role socialization. The stress exposure perspective, on the other hand, assumes that abnormal circumstances and events produce mental disorders and distress (Almeida and Kessler 1998). These two views are not necessarily impossible to resolve, but they continue to produce theoretical tensions.

Another factor producing distance between the sociology of mental health and the general field of sociology is the influence of social psychological theories on the field. As psychology has incorporated facets of the stress exposure perspective, sociologists of mental health have adopted ideas from social and developmental psychology on social support and relationships, coping, and life course development. An influential psychological perspective, the process of appraisal and coping, was developed by Lazarus and Folkman (1984), updated by Lazarus (1999), and has been further elaborated by Folkman and Moskowitz (2004). This perspective, dominant in the field of psychology, has emphasized how individual differences in perceptions of external stressors affect mental health. The focus of appraisal researchers on emotions as motivation for appraisal suggests commonality with biological research on emotion (Massey 2002). The theory of appraisal has been widely cited by sociologists who examine the impact of events on mental health (e.g.,Wethington and Kessler 1986).

The life course perspective (Elder 1974), now widely applied in the sociology of mental health (e.g., Wheaton and Clarke 2003; McLeod and Kaiser 2004), traces many of its components to the ecological perspective on human development pioneered by the developmental psychologist Urie Bronfenbrenner (1979). The life course perspective theorizes that developmental trajectories, developmental or socially normative timing of the stressor, and the accumulation of stressor exposure and resistance factors shape reaction to stressors (Elder, George, and Shanahan 1996). In the last decade, the life course perspective on stress accumulation has also been applied by psychologists, clinical psychologists, and neuroscientists (e.g., Singer and Ryff 1999; McEwen 2002; Repetti, Taylor, and Seeman 2002). Neuroscientists McEwen and Stellar (1993) have developed the concept of allostatic load which describes physiological mechanisms for the accumulated effects of past adaptation to stressors on health. Allostatic load is currently being adapted by sociologists to use in studies of stressor exposure across the life course and its relationship to mental health and disorder (Shanahan, Hofer and Shanahan 2003; Shanahan and Hofer 2005).

Sociological and psychological research streams on the relationship between stressor exposure and mental health are converging through collaborative efforts that examine the impact of stressor accumulation along the individual life course (Elder et al. 1996; Singer et al. 1998). A serious problem, however, is that most measures of stressor exposure available to researchers focus on recent exposures rather than the interactions of different types of stressor exposure over the long term; the majority of stressor exposure measures used in research are simple counts or sums of life events occurring over a short period of time (Wheaton 1999). Investigating the relationships between stressors over time and their combined associations with mental health and well-being is an important strategy for examining the impact of stressors over the life course (George 1999).

Issues of causality and theoretical approach are controversial in the field. Given the complexity and controversies in the sociology of mental health and illness, it is not surprising that one of the critical areas of the field is measurement. The two most disputed areas involve the measurement of outcomes and the measurement of stressor exposure.

The controversy begins with the outcomes. There is an increasing consensus that positive mental health and wellbeing is not just the absence of mental illness or disorder (Keyes 2002). There is also a controversy over whether dichotomous diagnoses of psychiatric disorder should be a proper outcome for sociological inquiry, in contrast to scales of distress symptoms (Kessler 2002; Mirowsky and Ross 2002).

Research diagnostic measures of mental disorder are controversial on many dimensions. Wakefield (1999) criticized the diagnostic measures used in the Epidemiological Catchment Area and National Comorbodity Studies for overestimating the prevalence of lifetime mental disorder in the United States. The NCS estimated that one-half of all Americans will suffer from a mental disorder over their lifetime (Kessler et al. 1994). A recent reanalysis of the NCS (Narrow et al. 2002), applying a standard of clinical seriousness based on other questions available in the survey, reduced the lifetime prevalence estimates significantly to 32 percent lifetime prevalence.

Another issue of controversy is whether a dichotomous outcome measure of disorder, one either has the disorder or not, misses levels of distress or poor social functioning that indicate considerable mental suffering (Kessler 2002; Mirowsky and Ross 2002). Persistent or recurring symptoms of sleeplessness, fatigue, sadness, loneliness, lack of appetite, and loss of interest in things in response to chronic stressors or unexpected life events can be unpleasant and disabling even if the sufferer does not show all of the symptoms of depression required for a diagnosis. The high threshold required for a diagnosis of disorder may understate emotional responses to events in the population at large. Whereas mental disorders may be relatively uncommon, symptoms of distress in response to life events are commonly observed and may indicate the presence of social dysfunction and strain in ways that surveys of mental disorders do not.

Measures of stressor exposure are particularly problematic in the sociology of mental health (Wheaton 1999). A complicating factor is that other mental health disciplines enforce higher standards of precision in measurement than does sociology. In addition, the majority of studies using stressor exposure measures do not account for any interaction between combinations of particular types of stressors. Applying the life course perspective model on mental health would ultimately require more sophisticated measures on how stressors combine and interact across time.

Both the biomedical and sociological streams of research on stress processes share an interest in environmental triggers of distress (Selye 1956). Following Selye, early stress researchers applied Selye’s assumption that all environmental threats activated the same or similar physiological response, using sums of exposures to different types of stressful events (Turner and Wheaton 1995). Almost immediately, sociologists and other social researchers modified this assumption, finding that more explicit and comprehensive measurement of the characteristics of stressors often increased the amount of variance explained in the mental health outcome. These measures included the estimated average “magnitude of change” scores in Social Readjustment Rating Scale (the SRRS: Holmes and Rahe 1967) and the Psychiatric Epidemiology Research Interview for Life Events (the PERI; Dohrenwend et al. 1978). Furthermore, it became clear that other characteristics of stressors, such as their type, timing, duration, severity, unexpectedness, controllability and impacts on other aspects of life make significant contributions to the stress response and mental health outcome (e.g., Brown and Harris 1978, 1989; Pearlin and Schooler 1978; Wethington, Brown, and Kessler 1995).

The stress exposure model is evolving to model the dynamic, continuous adaptation to stressors over time (e.g., Heckhausen and Schulz 1995; Lazarus 1999; Folkman and Moskowitz 2004). Sociologists have developed measures of chronic stress exposure (Pearlin and Schooler 1978) and exposure to stressors and hassles on a daily basis (Almeida, Wethington, and Kessler 2002). Researchers debate the relative reliability and validity of self-report checklist and interview measures of life events that include detailed probes that enable investigators to rate the severity of life events (Wheaton 1999). Most recently, psychologists have contributed to understanding variations in the relationships of different types of stressors (social loss vs. trauma and chronic vs. acute stressor exposure), to immune system function and cortisol activity (e.g., Dickerson and Kemeny 2004; Segerstrom and Miller 2004). Sociologists are now considering the potential for using measures of physiological activity (e.g., cortisol measurement) in their studies (Shanahan et al. 2003).

Applying the life course perspective to studying mental disorders and health over time has led to concern about the reliability and validity of retrospective measures of stressor exposure (Wethington et al. 1995; Wheaton 1999). Empirical research on memory for life events over a relatively short recall period is reassuring; most severe events can be recalled quite well over a 12-month retrospective period (Kessler and Wethington 1991). Serious concerns remain about longer retrospective recall periods. This concern is partially mitigated by the development of life history calendar methods, visual memory aids that can be used in interviews to enhance memory for life events (Freedman et al. 1988).

Despite the complexity of measurement, sociologists have pioneered the study of psychiatric sociology, or the epidemiology of mental disorders. The recent advances of measurement in the ECA and NCS studies have produced measures of outcomes that are scientifically accepted across disciplines (Cockerham 2005). These studies have also provided critical data on the use of mental health services by those who suffer from significant disorders and have had a major influence on other fields of study. The major epidemiological research questions have focused around the distribution of mental disorders and illnesses by social factors, including gender, socioeconomic status, marital status, race, and ethnicity. There is some, but more limited work, on factors such as ethnicity, migration, and location.

There is dispute whether the overall rate of mental disorders and illnesses differs by gender. The consensus before the publication of national data from the NCS was that men and women did not differ overall in rates of mental disorders; rather, different types of disorders are distributed differently. Women are more likely to report depressed affect and depressive disorders. Men, in turn, are more likely to report alcohol and drug disorders, violent behavior, and other indicators of acting out. Major psychoses such as schizophrenia and bipolar disorder are not distributed unequally by gender. There is now accumulating evidence that women are also more likely to report anxiety disorders (Kessler et al. 1994, 2005), which would mean that women are overall more likely to have mental disorders. Although there is continuing interest among biological and medical scientists to find a biological cause for women’s higher rates of some disorders, particularly depression, among sociologists social cause explanations still hold sway (e.g., Rosenfield 1999).

One of the most consistent findings in the epidemiology of mental disorders is that those of lower socioeconomic status are more likely to develop mental disorders (Cockerham 2005; Gallagher 2002). This general finding was confirmed by the NCS (Kessler and Zhao 1999). There is evidence, however, that those of higher statuses are more likely to suffer from affective disorders; the overrepresentation of mental disorders is due to higher rates of schizophrenia and some personality disorders among those of lower socioeconomic status.

Among sociologists of mental health, social causation theories continue to dominate, but more attention is being given to selection processes, especially the impact of mental disorders on upward economic mobility (e.g., Miech et al. 1999). Researchers who apply the life course perspective often study selection and economic mobility processes directly, most particularly those processes that affect educational attainment in early adulthood (e.g., McLeod and Kaiser 2004).

There remains considerable controversy in the literature whether members of racial minority groups report higher rates of mental disorder than majority racial groups. Given the relationship of socioeconomic status to mental health and disorders, it is logical to predict that rates of mental disorder in African Americans would be higher than the rates among white Americans because of the average lower socioeconomic status of blacks. Such a pattern would also reflect the additional burden of discrimination and prejudice and the impact such burdens have on mental well-being (Kessler, Mickelson, and Williams 1999).

The pattern of racial and ethnic differences, however, is more complex. For example, an analysis of risk and persistence of mental disorders among U.S. ethnic groups (Breslau et al. 2005) found that Hispanics reported lower lifetime prevalence of substance use disorders than whites, and that blacks reported lower lifetime prevalence of mood (depression or mania), anxiety, and substance use disorders. However, Hispanics were more likely to report persistent mood disorders (defined as recurrence of a past disorder), and blacks were more likely to report persistent mood and anxiety disorders. Research is needed on the factors that mitigate the impact of stressors on mental health of minority groups. Other researchers call for more attention to how mental disorders are measured and diagnosed in African Americans and other minority groups (e.g., Neighbors et al. 2003).

Although there is some evidence that pattern of mental distress by marital status may be changing as cohabitation becomes more socially accepted, the consensus still holds that married people are in better mental health and report fewer mental disorders than those who are not currently married. New research (Umberson and Williams 1999) points to the quality of the marital relationship as critical to mental well-being and health; those in unsatisfying or high-conflict marriages report poor mental health. Divorce is associated with poorer mental health over time, particularly among those who did not initiate the divorce.

Evidence such as that noted above is taken to mean that marriage confers benefits on mental health and may provide some protection against mental illness. Umberson and Williams (1999) note, however, that relatively little research has been done that has pitted the benefits of marriage perspective directly against the alternative social selection perspective that those who have mental disorders are less likely to marry or to remain married. Forthofer et al. (1996) estimated the relationship of age of onset of mental disorder on the probability of subsequent marriage. They found that those who have disorders are less likely to be married and when they marry have a higher risk of divorce. Unfortunately, studies that examine both social causation and social selection perspectives on marital status and mental health remain relatively rare, most likely because of the absence of satisfactory longitudinal data that can be used to address this issue.

One of the tensions in the sociology of mental health and illness is the interdisciplinary orientation of the field. Concepts are freely borrowed along the border of sociology and psychiatry/psychology. Much work is applied, or meant to be applied, to issues of importance to social policy, such as the social costs of untreated mental disorders. The life course perspective (Elder et al. 1996) is changing how research is done and how questions are being asked. New directions in the field include (1) a focus on comorbidity and severity of illness and its social impact, (2) the need for a closer connection between epidemiology and research on mental health services and policy, (3) the press to develop better measures of stressor exposure, (4) demand for more sophisticated measures and analyses of social resources, and (5) and the challenge of biological research on the stress process to the sociological study of mental health.

The study of comorbidity of mental disorders in people has transformed some aspects of the sociology of mental health. First, the documentation of comorbidity has influenced sociologists in the field to accept that mental illness is an objective reality. Second, it has become clear that those who are comorbid for multiple disorders are severely disabled in many important life roles. Their progress through life resembles the life path of “social selection.” Third, the acceptance that mental disorders are real physical entities, and the evidence for comorbidity are challenges to the environmental perspective on mental disorders. It is likely that those who have mental disorders attract or create stressor exposure (Eaton 2001). Thus, one major direction for sociological research in the future might be an emphasis on mental disorders as predictors, rather than outcomes, of social functioning and processes.

When reviewing the state of the sociology of mental health, Horwitz and Scheid (1999) observed that research on the social contexts of mental disorder and research on mental health services do not intersect all that much. They believed that this is because the two fields of research operate on different levels of analysis, one at the individual level and the other at the social or institutional level. A challenge for future research is to connect these two levels of analysis. Research on the social epidemiology of mental health and illness can inform organizations at all levels about the costs of untreated mental disorders to organizations and society in general.

As Wheaton (1999) observed, the social stress model requires considerable new development. This research paper has pointed out a number of methodological difficulties in measuring stressor exposure and the lack of fit between the most widely used measures of stressor exposure and the newly emerging life course perspective. Another advance would come through more detailed studies of how stressors are distributed in the population at large. Does the uneven distribution of stressors in the population “explain” the negative mental health outcomes for some groups? More research is needed in this area, ideally from the life course perspective, using longitudinal samples.

There is also a need for more research on the social distribution of resources that mitigate the impact of environmental challenges and stresses. Reviews of research on social support and social integration (e.g., Berkman and Glass 2000; Cohen et al. 2000; Pillemer et al. 2000) point out deficiencies in current measures of these resources. Do minority groups gain extra protection by asserting their identity and uniqueness? What is the social distribution of protective social resources? Do differences in distribution explain group differences in mental health?

The sociology of mental health is faced with a new challenge from the field of neuroscience. This research tends to be favored by federal funding agencies because of beliefs that neuroscience can lead to the discovery of new cures or therapeutic approaches to mental disorders. Neuroscience and its measurement equipment such as functional magnetic resonance imaging (fMRI) and cortisol sampling have the cachet of basic or “bench” science, while the observational and epidemiological approach of sociology is being portrayed as lower-quality science. However, the rise of neuroscience in research on mental disorders does not necessarily mean that social causes are irrelevant. The power of the new neuroscience of mental disorders is that it assumes there is an interaction between social factors and biological processes (McEwen 2002).

Yet there are serious impediments to the integration of sociological and biological research. One formidable impediment in sociology is the assumption that the biological perspective would reduce the entire stress process to individual differences in physical response, thus making environmental causation moot. Another impediment is that sociologists do not yet fully appreciate how much the biological approach to stress already incorporates measures of social context and stressors in studying adjustment to stressful events and situations (Singer and Ryff 1999). Sociologists (e.g., Pearlin et al. 1981) have long pointed out that the process of adjusting to stressors is a critical component of sociological and social psychological theories of the stress process (Thoits 1995). Thus, another challenge to sociologists of mental health is to incorporate techniques and measures that will powerfully represent the social context in multidisciplinary studies of mental health and mental disorders.

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Research Article

Impact of COVID-19 pandemic on mental health: An international study

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

¶ ‡ ATG, MK and AK designed and implemented the study together. AK and MK should be considered joint senior authors.

Affiliation Division of Clinical Psychology & Intervention Science, Department of Psychology, University of Basel, Basel, Switzerland

Roles Data curation, Formal analysis, Writing – original draft, Writing – review & editing

Affiliation Department of Health Sciences, European University Cyprus, Nicosia, Cyprus

Roles Investigation, Resources, Writing – review & editing

Affiliation Psychological Laboratory, Faculty of Public Health and Social Welfare, Riga Stradiņš University, Riga, Latvia

Affiliation Kore University Behavioral Lab (KUBeLab), Faculty of Human and Social Sciences, Kore University of Enna, Enna, Italy

Affiliation Department of Social Sciences, School of Humanities and Social Sciences, University of Nicosia, Nicosia, Cyprus

Affiliation Department of Nursing, Cyprus University of Technology, Limassol, Cyprus

Affiliation Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus

Affiliation Department of Psychological Counseling and Guidance, Faculty of Education, Hasan Kalyoncu University, Gaziantep, Turkey

Affiliation The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong

Affiliation Department of Psychology, Fundación Universitaria Konrad Lorenz, Bogotà, Columbia

Roles Conceptualization, Investigation, Resources, Writing – review & editing

Affiliation Faculty of Psychology, University of La Sabana, Chía, Columbia

Affiliation School of Applied Psychology, University College Cork, Cork, Ireland

Affiliation School of Psychology, University College Dublin, Dublin, Ireland

Affiliation Medical University Innsbruck, Innsbruck, Austria

Affiliation Department of Psychology, Babeş-Bolyai University (UBB), Cluj-Napoca, Romania

Affiliation Instituto Superior de Psicologia Aplicada (ISPA), Instituto Universitário; APPsyCI—Applied Psychology Research Center Capabilities & Inclusion, Lisboa, Portugal

Affiliation Faculdade de Psicologia, Alameda da Universidade, Universidade de Lisboa, Lisboa, Portugal

Affiliation LIP/PC2S, Université Grenoble Alpes, Grenoble, France

Affiliation Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cadiz, Spain

Affiliation Instituto ACT, Madrid, Spain

Affiliation Department of Psychology, European University of Madrid, Madrid, Spain

Affiliation Department of Psychology and Sociology, University of Zaragoza, Zaragoza, Spain

Affiliation Vadaskert Child and Adolescent Psychiatric Hospital, Budapest, Hungary

Affiliation Private Pratice, Poland

Affiliation Department of Psychology, University of Jyväskylä, Jyväskylä, Finland

Affiliation Clinic for Psychiatry, Clinical Center of Montenegro, Podgorica, Montenegro

Affiliation Ljubljana University Medical Centre, Ljubljana, Slovania

Affiliation Département de Psychologie, Université du Québec à Trois-Rivières, Trois-Rivières, Canada

Affiliation Department of Psychiatry and Behavioral Science, Duke University, Durham, North Carolina, United States of America

Roles Conceptualization, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Affiliation Department of Psychology, University of Cyprus, Nicosia, Cyprus

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• Andrew T. Gloster, 
• Demetris Lamnisos, 
• Jelena Lubenko, 
• Giovambattista Presti, 
• Valeria Squatrito, 
• Marios Constantinou, 
• Christiana Nicolaou, 
• Savvas Papacostas, 
• Gökçen Aydın, 

• Published: December 31, 2020
• https://doi.org/10.1371/journal.pone.0244809
• Reader Comments

The COVID-19 pandemic triggered vast governmental lockdowns. The impact of these lockdowns on mental health is inadequately understood. On the one hand such drastic changes in daily routines could be detrimental to mental health. On the other hand, it might not be experienced negatively, especially because the entire population was affected.

The aim of this study was to determine mental health outcomes during pandemic induced lockdowns and to examine known predictors of mental health outcomes. We therefore surveyed n = 9,565 people from 78 countries and 18 languages. Outcomes assessed were stress, depression, affect, and wellbeing. Predictors included country, sociodemographic factors, lockdown characteristics, social factors, and psychological factors.

Results indicated that on average about 10% of the sample was languishing from low levels of mental health and about 50% had only moderate mental health. Importantly, three consistent predictors of mental health emerged: social support, education level, and psychologically flexible (vs. rigid) responding. Poorer outcomes were most strongly predicted by a worsening of finances and not having access to basic supplies.

Conclusions

These results suggest that on whole, respondents were moderately mentally healthy at the time of a population-wide lockdown. The highest level of mental health difficulties were found in approximately 10% of the population. Findings suggest that public health initiatives should target people without social support and those whose finances worsen as a result of the lockdown. Interventions that promote psychological flexibility may mitigate the impact of the pandemic.

Citation: Gloster AT, Lamnisos D, Lubenko J, Presti G, Squatrito V, Constantinou M, et al. (2020) Impact of COVID-19 pandemic on mental health: An international study. PLoS ONE 15(12): e0244809. https://doi.org/10.1371/journal.pone.0244809

Editor: Joel Msafiri Francis, University of the Witwatersrand, SOUTH AFRICA

Received: October 3, 2020; Accepted: December 16, 2020; Published: December 31, 2020

Copyright: © 2020 Gloster et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by grants from the Swiss National Science Foundation awarded to Andrew T. Gloster (PP00P1_ 163716/1 & PP00P1_190082). The funder provided support in the form of salaries for authors [ATG], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. One of the authors is employed by a commercial affiliation: Private Pratice, Poland. This affiliation provided support in the form of salaries for authors [BK], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: One of the authors is employed by a commercial affiliation: Private Pratice, Poland. This affiliation provided support in the form of salaries for author BK, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials. No other authors have competing interests to declare.

Introduction

The COVID-19 global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus triggered governmentally mandated lockdowns, social distancing, quarantines and other measures in the interest of public health. The mandated lockdowns abruptly and dramatically altered people’s daily routines, work, travel, and leisure activities to a degree unexperienced by most people living outside of war zones. Simultaneously, the highly contagious, yet invisible virus transformed previously neutral situations to perceived potentially dangerous ones: social interaction, touching one’s face, going to a concert, shaking someone’s hand, and even hugging grandparents. Given these changes and looming threat, increases in anxiety and depression can be expected [ 1 ]. Indeed, common psychological reactions to previous quarantines include post-traumatic symptoms, confusion, and anger [ 2 ], though these data stem from quarantines of specific regions or a subgroup of exposed people, such as medical professionals. It therefore remains an empirical question whether such patterns are consistent when entire populations across the globe are simultaneously affected.

For most people, it stands to reason that governmentally mandated lockdowns decrease their activity levels and the number of stimuli experienced compared to pre-lockdown levels. The impact of reducing activities, stimuli and routines on the population is unknown, but various analogue situations can be used to make predictions, like death of a spouse [ 3 ]; hearing loss [ 4 ]; job loss [ 5 ]; long duration expeditions [ 6 ]; poor acculturation [ 7 ]; and even ageing when combined with loneliness [ 8 ]. Each of these situations is associated with increases in psychological distress. This reduction of stimulations may lead to boredom and reductions in reinforcement, which has been associated with depression [ 9 ]. The sum total of these literatures, and some evidence from country specific studies on COVID-19 suggests that for some people, the mental distress in the form of stress, depression, and negative affect are likely reactions to the lockdown; therefore, people’s wellbeing is likely to suffer. Indeed, increased loneliness, social isolation, and living alone are associated with increased mortality [ 10 ]–the exact effect that mandated lockdown and social distancing rules aimed to counteract.

Alternately, the planned slowing down of daily routines can be beneficial. For example, vacations and weekends are highly sought-after–if not always achieved–periods of relaxation and stress reduction [ 11 ]. Likewise, some religious and spiritual traditions encourage simplicity, mindfulness, and solitude with the goal of increasing wellbeing [ 12 ]. It is therefore conceivable that for some people the lockdown could offer a reprieve from daily hassles and stress and even lead to increases in wellbeing. It is therefore equally important to identify protective factors that can buffer against the negative effects of the lockdown.

Although nearly all people around the globe have been subject to some form of lockdown measures to contain the COVID-19 response, variations exist with respect to how each person is confined, even within a single country. For instance, during the COVID-19 pandemic some people were allowed to go to work, whereas others were required to work exclusively from home. For various reasons, some people had difficulty obtaining some basic supplies. Further, some were thrust into the situation of taking care of others (e.g., children, due to closing of schools). Finally, some people lost income as a result of the lockdown, and this is a known risk-factor for poor mental health [ 13 , 14 ]. Finally, a lockdown may be experienced differently the longer it continues and potentially when in confined spaces [ 2 ]. All of these lockdown-specific features may have an impact on one’s mental health, but to date it remains inadequately explored.

As the risk of the pandemic continues, it is important to understand to what degree the virus-induced uncertainty and the lockdown-induced changes in daily routines impact stress, depression, affect, and wellbeing. Towards this end, it is important to identify factors that can mitigate potential negative psychological effects of pandemics and lockdowns. Various social and psychological factors have been identified in other contexts that may also help build resilience in large-scale pandemics such as COVID-19. On the social level, one such candidate is social support, which has repeatedly been found to positively impact mental health and wellbeing [ 15 – 18 ]. Another social factor is the family climate and family functioning, which clearly impacts people’s mental health [ 19 , 20 ]. Psychological factors such as mindfulness and psychologically flexible response styles (as opposed to rigid and avoidant response styles) are behavioral repertoires that have previously been shown to buffer the impact of stress and facilitate wellbeing [ 21 – 24 ].

Given the scope of the COVID-19 pandemic, it is crucial to better understand how a pandemic and associated lockdowns impact on mental health. Thus, the aim of this study was to determine mental health outcomes and to examine known predictors of outcomes to identify psychological processes and contextual factors that can be used in developing public health interventions. It can be assumed, but remains untested, that those with risks in social-demographic factors, living conditions, social factors and psychological factors have more severe reactions to the lockdown. We therefore tested whether outcomes of stress, depression, affect, and wellbeing were predicted by country of residence, social demographic characteristics, COVID-19 lockdown related predictors, social predictors, and psychological predictors.

Participants

The inclusion criteria were ≥18 years of age and ability to read one of the 18 languages (English, Greek, German, French, Spanish, Turkish, Dutch, Latvian, Italian, Portuguese, Finnish, Slovenian, Polish, Romanian, Hong Kong, Hungarian, Montenegrin, & Persian.). There were no exclusion criteria. People from all countries were eligible to participate.

Ethics approval was obtained from the Cyprus National Bioethics Committee (ref.: EEBK EΠ 2020.01.60) followed by site approvals from different research teams involved in data collection. All participants provided written informed consent prior to completing the survey (computer-based, e.g., by clicking “yes”).

A population based cross-sectional study was conducted in order to explore how people across the world reacted to the COVID-19. The anonymous online survey was distributed using a range of methods. Universities emailed the online survey to students and academic staff and also posted the survey link to their websites. In addition, and in order to broaden the sample to older age groups and to those with different socio-demographic characteristics, the survey was disseminated in local press (e.g., newspapers, newsletters, radio stations), in social media (e.g., Facebook, Twitter, etc.), in professional networks, local hospitals and health centers and professional groups’ email lists (e.g., medical doctors, teachers, engineers, psychologists, government workers), and to social institutions in the countries (e.g., churches, schools, cities/townships, clubs, etc.).

Data were collected for two months between 07th April and 07th June 2020. The majority of countries where data were collected had declared a state of emergency for COVID-19 during this time.

Well validated and established measures were used to assess constructs. When measures did not already exist in a language, they were subject to forward and backward translation procedures. Well-validated measures of predictors and outcomes and items measuring COVID-19 related characteristics were selected after a consensus agreement among the members of this study.

Respondents’ countries were coded and entered as predictors.

Socio-demographic status.

Participants responded to questions related to their socio-demographic characteristics including their age, gender, country of residence, marital status, employment status, educational level, whether they have children as well as their living situation.

Lockdown variables.

Participants responded to questions related to lockdown including length of lockdown, whether they need to leave home for work, any change in their finances, whether they were able to obtain basic supplies, the amount of their living space confined in during the lockdown. They were also asked whether they, their partner, or a significant other was diagnosed with COVID-19.

Social factors.

Social factors were measured using the Brief Assessment of Family Functioning Scale (BAFFS; [ 25 ]) and the Oslo Social Support Scale (OSSS; [ 26 ]). The BAFFS items are summed to produce a single score with higher scores indicating worse family functioning. The OSSS items are summed up and provide three levels types of social support: low (scored 3–8), moderate (scored 9–11) and high (scored 12–14).

Psychological factors.

Psychological factors including mindfulness and psychological flexibility. Mindfulness was measured using the Cognitive Affective Mindfulness Scale (CAMS; [ 27 ]). The CAMS produces a single score with higher scores indicating better mindfulness qualities. Psychological flexibility (e.g., hold one’s thoughts lightly, be accepting of one’s experiences, engage in what is important to them despite challenging situations) was measured using the Psyflex scale [ 28 ]. The Psyflex produces a single score with higher scores indicating better psychological flexibility qualities.

Stress was measured using the Perceived Stress Scale (PSS; [ 29 ]). The PSS assesses an individual’s appraisal of how stressful situations in their life are. Items ask about people’s feelings and thoughts during the last month. A total score is produced, with higher scores indicating greater overall distress.

Depression.

Depressive symptomatology was assessed using two items from the disengagement subscale of the Multidimensional State Boredom Scale (MSBS; [ 30 ]). These items assessed wanting to do pleasurable things but not finding anything appealing (i.e., boredom), as well as wasting time. Based on concepts of reinforcement deprivation (i.e., lack of access to or engagement with positive stimuli) that is known to contribute to depression, we added an item that measured how rewarding or pleasurable people found the activities that they were engaging in (i.e., reinforcement). Higher scores indicated higher depressive symptomatology.

Positive affect/ negative affect.

The Positive And Negative Affect Scale (PANAS) was used to measure affect [ 31 ]. The original version of the questionnaire was used with five additional items: bored, confused, angry, frustrated and lonely. All items were scored on a 5-point Likert type scale, ranging from 1 = very little/not at all to 5 = extremely and summed up so that higher scores in the positive-related items indicating higher positive affect and higher scores in the negative-related items indicating higher negative affect. In order to capture additional dimensions of negative affect believed to be relevant to the COVID-19 lockdowns, we additionally added five items: bored, confused, angry, frustrated, lonely.

Wellbeing was assessed using the Mental Health Continuum Short Form (MHC-SF; [ 32 ]); which assesses three aspects of wellbeing: emotional, psychological, and social. The MHC-SF produces a total score and scores for each of the three aspects of wellbeing. The MHC-SF can also be scored to produce categories of languishing (i.e., low levels of emotional, psychological, and social well-being), flourishing (i.e., high levels of emotional psychological and social well-being almost every day), and moderately mentally healthy (in between languishing and flourishing).

Statistical analysis

The mean and standard deviation was calculated for dependent variables that follow the normal distribution while the median and interquartile range (IQR) were computed for non-normally distributed data. Bivariable association between an outcome variable and each predictor was investigated with ANOVA test for categorical predictor and univariable linear regression for numerical predictor. Linear mixed-effect model with random effect for country was performed to consider simultaneously several predictors in the same model and to account for the variation in outcome variable between countries. Four separate linear mixed-effect models were used for each outcome variable, one for each set of socio-demographic, lockdown, social and psychosocial predictors and multicollinearity for each set of predictors was investigated with the variation inflation criterion (VIF). Standardized regression coefficients were computed as effect size indices to measure the strength of the association between predictor variables and outcome variables. The comparison between the country mean and overall mean for each outcome variable was estimated though a linear regression model with dependent variable the mean centering outcome and predictor the country. Cohen’s d effect size of the standardize difference between country mean and the overall mean was computed as a measure of the magnitude of the difference between the two means.

The whole sample was used in linear mixed-effect models while for the comparison of country mean to the overall mean was used the sample from countries with sample size ≥100. The R packages lme4 and effect sizes were used for fitting the linear mixed effect model and to compute the standardized regression coefficients of the linear mixed effect models [ 33 ]. Significance test and confidence intervals were calculated at a significance level of 0.05. The following cut-off values were used for the evaluation of the effect sizes: ‘tiny’ ≤0.05, ‘very small’ from 0.05 to ≤0.10, ‘small’ from 0.10 to ≤ 0.20, ‘medium’ from 0.20 to ≤ 0.30, ‘large’ from 0.30 to ≤ 0.40 and ‘very large’ > 0.40 [ 34 ].

Descriptive

Participants were n = 9,565 people from 78 countries. See supporting information for a participation flowchart ( S1 Appendix ). The countries with the largest samples were: Latvia (n = 1285), Italy (n = 962), Cyprus (n = 957), Turkey (n = 702), Switzerland (n = 550), Hong Kong (n = 516), Colombia (n = 485), Ireland (n = 414), Austria (n = 368), Romania (n = 339), Portugal (n = 334), France (n = 313), Spain (n = 296), Germany (n = 279), Hungary (n = 273), Greece (n = 270), USA (n = 268), Finland (n = 157), Montenegro (n = 147), Poland (n = 135), United Kingdom (n = 100), Slovenia (n = 77), and Canada (n = 60). The remaining countries are listed in the supporting information ( S1 Table ).

Outcome variables

The means, standard deviations, and where appropriate percentage of participants within categories of the five outcome variables can be seen in Table 1 .

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https://doi.org/10.1371/journal.pone.0244809.t001

Predictor variables

A full list of countries can be found in the supporting information ( S1 Table ).

The mean age was 36.9 (13.3) years. A majority of participants were female (77.7%), approximately a fifth male (22.0%), and small minority identified as other (0.3%). More than half of the respondents were either in a relationship (25.7%) or married (36.1%), almost a third were single (30.8%), and the rest were either divorced (5%), widower (1.1%) or other (1.3%). Participants indicated that they lived: alone (14.6%), with both parents (20.8%), one parent (5.1%), with their own family including partner and children (54.1%), or with friends or roommates (5.5%). Less than half of respondents had children (40.8%). Approximately half of the participants were working full time (53.4%), almost a fifth were working part-time (17.5%), 23.2% were unemployed and a small minority were either on parental leave (2.2%) or retired (3.7%).

COVID-19 lockdown variables.

At the time of responding, participants were in lockdown or self-isolation for a median of 5.0 (3.0 IQR) weeks. Most people indicated that they had not been infected with COVID-19 (88.0%), a small minority indicated they had been infected (1.4%) and the rest had symptoms but were unsure (10.6%). Similar patterns were seen with reported infection rates of partners (no: 92.2%, yes: 0.7%, unsure: 7.1%) and of people close to them (no: 86.0%; yes: 5.6%; unsure: 8.4%). With respect to leaving the house for work, almost half (47.7%) indicated that this never occurred, 7.7% indicated leaving only once, whereas an almost equal number indicated leaving a couple times per week (23.7%) or more than three times per week (21.0%). Nearly all participants indicated they were able to obtain all the basic supplies they needed (93.5%). Participants reported having a median inner living space of 90.0 square meters (80.0 IQR) and median outdoor space of 20.0 square meters (192.1 IQR). Finally, with respect to finances, more than half indicated that their financial situation remained about the same (57.9%), a minority indicated it improved (8.9%), and a third reported that their finances had gotten worse (33.3%).

Social and psychological predictors.

Mean values of the other predictors (i.e., social predictors and psychological predictors) can be seen in Table 1 .

Multivariate analyses

Results of multivariate analyses for the outcome of stress can be seen in Table 2 . The largest protective factor against stress was social support (high support vs low support (-3.35, 95%CI, -3.39 to -2.92), with a very large effect size). Positive predictors of stress with large effect sizes were being female (2.42, 95%CI, 2.07 to 2.77) and worsening of finances (2.32, 95%CI, 1.68 to 2.96), whereas psychological flexibility buffered this response (-0.65, 95%CI, -0.69 to -0.62). Higher education levels were also associated with lower levels of stress, with a large effect size (see Table 2 ). Moderate effect sizes for predictors associated with less stress were older age (-0.13, 95%CI, -0.14, -0.11) and mindfulness (-0.69, 95%CI, -0.74, -0.64). Moderate effect sizes of predictors associated with more stress were worse family functioning (0.98, 95%CI, 0.90, 1.06) and not being able to obtain all basic supplies (1.82 95%CI, 1.12, 2.52).

https://doi.org/10.1371/journal.pone.0244809.t002

Differences in reported levels of stress across countries were largely negligible, with the exception of two countries that reported higher levels of stress (Hong Kong (2.85, 95%CI, 2.22, 3.49) and Turkey (2.47, 95%CI, 1.93, 3.02)) and two that reported lower levels of stress (Portugal (-2.50, 95%CI, -3.29, -1.71) and Montenegro (-3.30, 95%CI, -4.49, -2.11)) than the average stress level across all countries. See supporting information for information on each country ( S2 – S6 Tables).

Results of multivariate analyses for the outcome of depression can be seen in Table 3 . The strongest predictor of depression was social support, such that high (-1.30, 95%CI, -1.44, -1.16) and medium levels (-0.73, 95%CI, -0.85, -0.62) of social support were protective against depression (relative to low levels) with a very large and large effect sizes, respectively. The only other large effect size was for psychological flexibility, which also served in a protective manner (-0.20, 95%CI, -0.22, -0.19). Moderate effect sizes of predictors associated with less depression symptoms were also observed for higher education levels (see Table 3 ). Moderate effect sizes of predictors associated with more depression were worse family functioning (0.29, 95%CI, 0.27, 0.32) and not being able to obtain all basic supplies (0.49, 95%CI, 0.27, 0.70).

https://doi.org/10.1371/journal.pone.0244809.t003

The amount of depression symptoms reported on average within countries was similar for most countries with the exception of one country with lower reported levels than average with a large effect size (Austria (-0.71, 95%CI, -0.95, -0.47)) and one with higher levels than average with a large effect size (USA (0.85, 95%CI, 0.58, 1.13)). See supporting information for information on each country ( S2 – S6 Tables).

Results of multivariate analyses for the outcome of affect can be seen in Table 4 . With respect to positive affect, social support (high support vs low support (5.69, 95%CI, 5.23, 6.16) and psychological flexibility (0.77, 95%CI, 0.74, 0.81) were both predictors with very large effect sizes. Interestingly, those who left their house more than three times per week had higher levels of positive affect than those that did not leave their house for work (1.68, 95%CI, 1.18, 2.17), with a medium effect size. Higher education levels were associated with higher levels of positive affect with a medium to large effect size (see Table 4 , PANAS-Positive).

https://doi.org/10.1371/journal.pone.0244809.t004

The amount of positive affect reported on average within countries was similar for most countries with the exception of one country with lower reported levels than average with a large effect size (Finland (-2.96, 95%CI, -4.19, -1.73)) and one with higher reported levels than average with a large effect size (Portugal (2.96, 95%CI, 2.12, 3.80)). See supporting information for information on each country ( S2 – S6 Tables).

With respect to negative affect, social support (high support vs low support (-2.74, 95%CI, -3.2, -2.29) and psychological flexibility (-0.62, 95%CI, -0.66, -0.58) were again the strongest associated predictors, with large effects. Higher education levels were also associated with lower levels of negative affect, with a medium effect (see Table 4 , PANAS-Negative). Higher levels of negative affect were noted, with medium effect sizes, for the predictors: worsening of finances (1.75, 95%CI, 1.10, 2.40) and not being able to obtain all basic supplies (1.6, 95%CI, 0.89, 2.31).

The amount of negative affect reported on average within countries was similar for most countries with the exception of few countries with lower reported negative affect levels than average with a very large effect sizes (Switzerland (-4.96, 95%CI, -5.91, -4.01), Germany (-4.70, 95%CI, -6.03, -3.37) & Austria (-6.49, 95%CI, -7.65, -5.33)) and one with a large effect size (Montenegro (-3.56, 95%CI, -5.39, -1.73). The average amount of negative affect was higher than average in two countries, with very large effects size (Turkey (5.75, 95%CI, 4.92, 6.59) & Finland (7.57, 95%CI, 5.80, 9.34)). See supporting information for information on each country ( S2 – S6 Tables).

Results of multivariate analyses for the outcome of wellbeing can be seen in Table 5 . Once again, social support (high support vs low support (13.20, 95%CI, 12.39, 14.01)) and psychological flexibility (1.42, 95%CI, 1.34, 1.49) were the predictors with the largest effect sizes (very large) on wellbeing. Higher education levels were associated with higher levels of wellbeing with a medium to large effect sizes (see Table 5 ). Medium negative effect sizes were noted for family functioning (-1.98, 95%CI, -2.12, -1.83) and inability to obtain all basic supplies (-3.27, 95%CI, -4.67, -1.87). Two medium positive effect sizes were observed: mindfulness (0.95, 95%CI, 0.86–1.04) and living with friends/roommates ((3.04, 95%CI, 1.59, 4.48), relative to living alone).

https://doi.org/10.1371/journal.pone.0244809.t005

The level of wellbeing reported on average within countries was similar for most countries with the exception of three countries with higher levels with large effect sizes (Austria (4.95, 95%CI, 3.55, 6.34), Finland (5.24, 95%CI, 3.10, 7.38), & Portugal (4.59, 95%CI, 3.12, 6.05)) and two countries with lower levels of wellbeing than average with large (Italy (-4.36, 95%CI, -11.06, 2.35)) and very large effect sizes (Hong Kong (-6.84, 95%CI, -8.02, -5.66)). See supporting information for information on each country ( S2 – S6 Tables).

The COVID-19 is the largest pandemic in modern history. This study assessed nearly 10,000 participants across many countries to examine the impact of the pandemic and resultant governmental lockdown measures on mental health. During the height of the lockdown, the pandemic was experienced as at least moderately stressful for most people, and 11% reported the highest levels of stress. Symptoms of depression were also high, including 25% of the sample indicating that the things they did were not reinforcing, 33% reporting high levels of boredom, and nearly 50% indicating they wasted a lot of time. Consistent with symptoms of stress and depression, 10% of participants were psychologically languishing. These results suggest that there is a subgroup of people who are especially suffering and that in about 50% of the respondents’ levels of mental health was only moderate. Previous studies have found that along with low levels, even moderate levels of mental health (which consists of only moderate levels of emotional, psychological, and social well-being) are associated with increased subsequent disability, productivity loss, and healthcare use [ 35 – 37 ]. Not everyone was suffering, however, as evidenced by the nearly 40% of participants who reported levels of mental health consistent with flourishing. The present results, while serious, do not point to more severe reactions observed in previous samples of selective quarantined individuals or groups [ 2 ]. Perhaps the previously reported distress in these groups is prevented when an entire country or world is in lockdown so that the feeling emerges that “everyone is in it together”.

Importantly, a handful of predictors emerged that consistently predicted all outcomes: Social support, education level, finances, access to basic needs, and the ability to respond psychologically flexible. The consistency of results examining predictors is noteworthy, both in terms of the consistently strong predictors (e.g., social support, education, psychological flexibly, as well as loss of income and lack of access to necessities) and in terms of the other predictors that were either not predictive or only weakly so. All predictors were chosen based on theoretical ties to the outcomes, previous findings, and studies on quarantines [ 2 ].

A novel finding was that people who left their house three or more times per week reported more positive affect than those that left their house less often. It is possible that these people experienced more variation, which contributed to positive affect. It is also possible they experienced a greater sense of normality. Future studies are encouraged to further investigate possible mechanisms through which this result unfolds.

Overall, these patterns did not differ substantially between countries. Although some differences did emerge, they were mostly inconsistent across outcomes. Three countries fared worse on two outcomes each: Hong Kong (stress & wellbeing); Turkey (stress & negative affect); and Finland (lower positive affect and higher negative affect)–though participants in Finland also reported higher levels of wellbeing than average. Two countries had more favorable outcomes than the average levels across all countries: Portugal (lower stress and higher wellbeing) and Austria (lower depression and higher wellbeing). The differences observed are likely due to a combination of chance, sampling, nation specific responses to the COVID-19 pandemic, cultural differences, and other factors playing out in the countries (e.g., political unrest [ 38 ]). If replicated, future studies are encouraged to examine possible mechanisms of these outcomes.

This study provides valuable insights on several levels. First, it documents the mental health outcomes across a broad sample during the COVID-19 global pandemic. Second, it informs about the conditions and resilience factors (social support, education, and psychological flexibility) and risk factors (loss of income and inability to get basic supplies) that affect mental health outcomes. Third, these factors can be used in future public health responses are being made, including those that require large scale lockdowns or quarantines. That is, public health officials should direct resources to identifying and supporting people with poor social support, income loss, and potentially lower levels of education and provide a strategy to mitigate special risks in these subpopulations. The importance of social support needs to be made clear to the public and to the degree possible mechanisms that can contribute to social support should be supported. Further, psychological flexibility is a trainable set of skills that has repeatedly been shown to ameliorate suffering [ 22 , 39 ]; and can be widely distributed with modern technological intervention tools such as digital, internet, or virtual means [ 40 ]. We do not claim, however, that psychological flexibility is the only factor that can be used for interventions. Instead, it is a recognized transdiagnostic factor assessed in this study and one that is feasible to be targeted and modified by interventions and prevention [ 41 – 43 ].

This study is limited by several important factors. First, the results are based on cross sectional analysis and correlations. As such, causation cannot be inferred and any delayed impact of the pandemic and lockdown on peoples’ mental health was not captured. Second, all results of this survey were obtained via self-report questionnaires, which can be subject to retrospective response bias. Third, although the sample was large and based on varied recruitment sources, it was not representative of the population and undersampled people who suffered most from the pandemic (i.e., front line health care professionals, people in intensive care, etc.) or people without internet access, etc. Finally, the country-specific incidence rates and lockdown measures differed across countries. These were not assessed, but future studies are encouraged to investigate how such factors impact mental health outcomes.

These limitations notwithstanding, based on nearly 10,000 international participants, this study found that approximately 10% of the population was languishing during or shortly after the lockdown period. These finding have implications for public health initiatives. First, officials are urged to attend to, find, and target people who have little social support and/ or whose finances have worsened as a result of the measures. Second, public health interventions are further urged to target psychological processes such as psychological flexibility in general to potentially help buffer other risk factors for mental health. Likewise, availability of social support and information about where to get support and remain connected are needed. These recommendations should become part of public health initiatives designed to promote mental health in general, and should equally be considered when lockdowns or physical distancing are prescribed during a pandemic.

Supporting information

S1 table. list of all countries included in the data set..

https://doi.org/10.1371/journal.pone.0244809.s001

S2 Table. Geodemographic predictors for Perceived Stress Scale.

https://doi.org/10.1371/journal.pone.0244809.s002

S3 Table. Geodemographic predictors for MSBS–depression.

https://doi.org/10.1371/journal.pone.0244809.s003

S4 Table. Geodemographic predictors for PANAS positive.

https://doi.org/10.1371/journal.pone.0244809.s004

S5 Table. Geodemographic predictors for PANAS negative.

https://doi.org/10.1371/journal.pone.0244809.s005

S6 Table. Geodemographic predictors for MHCSF—mental health continuum.

https://doi.org/10.1371/journal.pone.0244809.s006

S1 Appendix. Participation flowchart.

https://doi.org/10.1371/journal.pone.0244809.s007

Acknowledgments

We wish to thank the following people for their work in helping to implement the study: Spyros Demosthenous, Christiana Karashali, Diamanto Rovania (University of Cyprus); Maria Antoniade (European University of Cyprus); Ioanna Menoikou (Cyprus University of Technology); Elias Ioannou (University of Nicosia); Sonja Borner, Victoria Firsching-Block, Alexander Fenn (University of Basel); Cristīne Šneidere, Ingrīda Trups-Kalne, Lolita Vansovica, Sandra Feldmane, (Riga Stradiņš University); David Nilsson (Lund University); Miguel A. Segura-Vargas (Fundación Universitaria Konrad Lorenz); Claudia Lenuţa Rus, Catalina Otoiu, Cristina Vajaean (Babes-Bolyai University). We further wish to thank Fabio Coviello and Sonja Borner (University of Basel) for their help in preparing the manuscript.

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• Research article
• Open access
• Published: 01 May 2020

A systematic literature review of existing conceptualisation and measurement of mental health literacy in adolescent research: current challenges and inconsistencies

• Rosie Mansfield   ORCID: orcid.org/0000-0002-8703-5606 1 ,
• Praveetha Patalay 2 &
• Neil Humphrey 1  

BMC Public Health volume  20 , Article number:  607 ( 2020 ) Cite this article

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With an increased political interest in school-based mental health education, the dominant understanding and measurement of mental health literacy (MHL) in adolescent research should be critically appraised. This systematic literature review aimed to investigate the conceptualisation and measurement of MHL in adolescent research and the extent of methodological homogeneity in the field for meta-analyses.

Databases (PsycINFO, EMBASE, MEDLINE, ASSIA and ERIC) and grey literature were searched (1997–2017). Included articles used the term ‘mental health literacy’ and presented self-report data for at least one MHL domain with an adolescent sample (10–19 years). Definitions, methodological and contextual data were extracted and synthesised.

Ninety-one articles were identified. There was evidence of conceptual confusion, methodological inconsistency and a lack of measures developed and psychometrically tested with adolescents. The most commonly assessed domains were mental illness stigma and help-seeking beliefs; however, frequency of assessment varied by definition usage and study design. Recognition and knowledge of mental illnesses were assessed more frequently than help-seeking knowledge. A mental-ill health approach continues to dominate the field, with few articles assessing knowledge of mental health promotion.

Conclusions

MHL research with adolescent samples is increasing. Results suggest that a better understanding of what MHL means for this population is needed in order to develop reliable, valid and feasible adolescent measures, and explore mechanisms for change in improving adolescent mental health. We recommend a move away from ‘mental disorder literacy’ and towards critical ‘mental health literacy’. Future MHL research should apply integrated, culturally sensitive models of health literacy that account for life stage and acknowledge the interaction between individuals’ ability and social and contextual demands.

Peer Review reports

Around 50% of mental health difficulties have their first onset by age 15 [ 1 , 2 ] and are associated with negative outcomes such as lower educational attainment and physical health problems [ 3 ]. Approximately 10–20% of young people are affected worldwide, and many more will experience impairing mental distress at varying degrees across the mental health continuum [ 4 , 5 , 6 , 7 , 8 ]. Adolescence is a critical period of transition, characterised by physical, cognitive, emotional, social and behavioural development [ 9 ]. It has therefore been identified as a particularly important developmental phase for improving ‘mental health literacy’ (MHL) and promoting access to mental health services [ 10 , 11 ]. However, better understanding of the conceptualisation and measurement of MHL in this population is needed.

MHL was first defined as ‘ knowledge and beliefs about mental disorders which aid their recognition, management or prevention’ ( [ 12 ] pp 182) and consisted of six domains: ‘1) the ability to recognise specific disorders or different types of psychological distress; 2) knowledge and beliefs about risk factors and causes; 3) knowledge and beliefs about self-help interventions; 4) knowledge and beliefs about professional help available; 5) attitudes which facilitate recognition and appropriate help-seeking, and 6) knowledge of how to seek mental health information’ ( [ 13 ] pp 396). Domains were later revised to include early recognition, prevention and mental health first aid skills [ 14 ]. The most recent definition comprises four broad domains aligned with current definitions of health literacy: ‘1) understanding how to obtain and maintain positive mental health; 2) understanding mental disorders and their treatments; 3) decreasing stigma related to mental disorders, and 4) enhancing help-seeking efficacy (knowing when and where to seek help and developing competencies designed to improve one’s mental health care and self-management capabilities’ ( [ 15 ] pp 155).

In a review of MHL measurement tools, O’Connor et al. revealed that the most commonly assessed domain was recognition of mental disorders. No studies assessed either knowledge of how to seek information or knowledge of self-help interventions [ 16 ]. The focus on recognition of mental disorders, along with knowledge about risk factors, causes and appropriate treatments, has been criticised for promoting the psychiatric and biogenetic conceptualisation of mental illness [ 17 , 18 ]. Despite being found to reduce blame, biogenetic explanations and attributions can lead to misconceptions about dangerousness and unpredictability and pessimism about recovery [ 19 ]. Early research also suggested that biogenetic causal theories increase a desire for social distance [ 20 , 21 ]. MHL modelled on recognition of psychiatric labels, and diagnostic language such as ‘disorder’, often leads to psychosocial predictors being ignored, and more negative attitudes towards individuals experiencing mental distress [ 22 , 23 ].

These criticisms, in line with broader socio-cultural approaches to literacy [ 24 ] understand MHL as a socio-political practice used to communicate, and make dominant, the psychiatric discourse. This appears to undermine attempts to reduce stigma, the most common outcome of school-based MHL interventions [ 25 ]. In their review of MHL measurement tools, O’Connor et al. excluded all disorder specific scales, claiming that ‘ MHL by definition should encompass knowledge and attitudes relating to a range of mental health disorders and concepts .’ ( [ 16 ] pp 199). Chambers et al. further criticised current MHL definitions for being narrow in focus with a predominantly mental-ill health approach, ignoring the complete mental health state that goes beyond the dichotomy of illness and wellness [ 26 , 27 ]. The difference between literacy about mental disorders and the ability to seek out, comprehend, appraise and apply information relating to the complete mental health state is an emerging point of discussion, and has seen MHL re-defined to include self-acquired knowledge and skills relating to positive psychology [ 28 , 29 ]. This aligns with the World Health Organisation’s (WHO) definition of mental health, which includes subjective wellbeing, optimal functioning and coping, and recognises mental health beyond the absence of disorder [ 30 ].

In response to increasingly inclusive definitions of MHL, Spiker and Hammer presented the argument for MHL as a ‘multi-construct theory, rather than a multi-dimensional construct’ ( [ 31 ] pp 3). The proposal suggested that by stretching the MHL construct, researchers have reduced the consistent use of the definition across studies, resulting in heterogeneous measurement [ 32 ]. Reviews of the psychometric properties of MHL measurement tools support this argument, and conclude that more consistent measurement with valid scales is needed [ 33 , 34 , 35 , 36 ]. Spiker and Hammer also outline problems with construct irrelevant variance [ 31 ], in which measures capture more than they intended to. Furthermore, they note that construct proliferation or the ‘jingle jangle fallacy’ [ 37 ], in which scales may have different labels but measure the same construct, and vice versa, increase problems with discriminant validity. Understanding MHL as a multi-construct theory could help delineate between its broad domains: recognition, knowledge, stigma and help-seeking beliefs, and acknowledge their complexity.

Internationally, there is growing political interest in child and adolescent mental health promotion and education [ 6 , 38 ]. Despite limited evidence, it is suggested that educating the public by improving their ability to recognise mental disorders, and increasing help-seeking knowledge, can promote population mental health [ 39 , 40 ]. Furthermore, a reduction in stigmatising attitudes is consistently reported to improve help-seeking [ 41 , 42 ]. MHL, by definition, includes these interacting domains. However, despite a comprehensive set of reviews that assess the psychometric properties of MHL measurement tools [ 33 , 34 , 35 , 36 ], there is no systematic literature review, to date, that assesses the current conceptualisation and measurement of MHL across adolescent research. Being able to clearly operationalise what is meant by a MHL intervention and meta-analyse their effectiveness, will have implications for the investment in school and population level initiatives. Similarly, being able to conduct time trend analyses that plot possible improvements in adolescents’ MHL against mental health outcomes, will reveal the extent to which population level improvements in MHL promote mental health. First though, we must have a clear picture of the understanding of MHL in adolescent research and how it is currently being measured.

Objectives and research questions

The aim of the current study was therefore to examine the ways in which MHL has been conceptualised and measured in adolescent research to date, and explore the extent of methodological homogeneity in the field for meta-analyses. We set out to answer the following research questions: 1) What are the most common study designs, contexts, and aims? 2) How is MHL conceptualised? 3) What are the most commonly measured domains of MHL, and do these vary by study design and definition usage? 4) To what extent do articles use measures that have evidence of validity for use with adolescent samples? 5) Is there enough methodological homogeneity in the field to conduct meta-analyses?

A protocol was published on PROSPERO in December 2017 (reference: CRD42017082021 ), and was updated periodically to reflect the progress of the review. Relevant PRISMA guidelines for reporting were followed [ 43 ].

Eligibility criteria

Articles were included with adolescent samples aged between 10 and 19 [ 44 ]. Samples with a mean age outside of this range were excluded. If no mean was presented and the age range fell outside of the criterion, articles were only included if results were presented for sub-groups (e.g. 12–17 years from a sample aged 12–25). General MHL and diagnosis-specific literacy research was included. Articles with quantitative study designs and extractable self-report data for at least one time point measurement of any MHL domain were eligible. These criteria ensured that only articles with extractable data from adolescents, who had not yet received any form of intervention were included. At the full text screening phase, articles published before 1997, based on the date of the first MHL definition [ 12 ], and those that did not explicitly use the term ‘mental health literacy’ or a diagnosis-specific equivalent (e.g. ‘depression literacy’) were excluded. By applying this criterion, the current study was able to present the number of articles that measured domains without referring to MHL. Identifying cases where researchers measure the same construct but use different labels is important when considering conceptualisation and meta-analyses.

Only articles available in English were included. Specific populations such as clinical/patient populations and juvenile offenders were excluded, as were university students. In contrast to schools in most countries, universities are not universal, with only a sub-set of young people entering higher education. University samples were therefore not seen as representative and often included participants outside the age criterion. Post-partum and later life neurocognitive disorders (e.g. Alzheimer’s disease) were removed given their limited relevance for this age group. In line with other MHL reviews [ 33 ], articles with a focus on substance abuse were excluded to avoid reviewing a large number of adolescent risk behaviour studies and substance abuse prevention programmes.

Search strategy

The search strategy was developed to include a number of combinations of terms to ensure that literature relating to different domains of MHL were captured. Population terms such as ‘adolescen*’ or ‘young people*’ had to be present and mental health related terms (e.g. ‘mental health’ and ‘mental disorders’) were exploded to capture general MHL and diagnosis-specific studies. Similarly, outcome terms (e.g. ‘health literacy’ and ‘health education’) were exploded, and domain specific terms included (e.g. ‘knowledge’, ‘recogni*’, ‘attitud*’, ‘stigma*’, ‘help-seek*’, ‘prevent*’ or ‘positive*’). See Additional File 1 . for an example search strategy.

Data sources

The following databases were searched from their start date to the search dates (November 2017): PsycINFO, EMBASE, MEDLINE, ASSIA, and ERIC. Key authors were also contacted to identify grey literature. References were harvested from related reviews and all papers identified in the search. Hand searches of key authors’ publication lists were also conducted, and Google Scholar was used to find studies known by the authors but not identified in the database searches.

Article selection

Results from the database searches were saved to Endnote and duplicates were removed. The lead author screened the article titles and abstracts to identify those that met the inclusion criteria. Full texts were then screened and reasons for exclusion were recorded. Any uncertainties were resolved through discussion with other members of the research team. A sub-set of 20 articles were screened at full text stage by the third author, and a strong level of agreement was found (k = .78, p  = .001).

Data extraction

Research was assessed on an article level (rather than by study) for the purposes of investigating the conceptualisation of MHL. The fact that authors break MHL down into component parts to write separate articles is support for identifying which domains are more commonly associated with the use of the term. Data on the following methodological factors were extracted from eligible articles using a uniform data extraction form: year of publication, country and setting (community (research conducted outside of the school setting e.g. population level surveys) vs. school-based research), study design (intervention vs. population-based), primary aims, MHL definition and use of the term, general MHL vs. diagnosis-specific literacy, number/types of MHL domains measured, and measurement tools (e.g. vignette, yes/no, Likert scales).

Data analysis

A content analysis was conducted using NVivo 12 to organise articles by their primary aim and understand the conceptualisation of MHL based on the definition presented and use of the term. Frequencies and percentages for each group were calculated and articles coded based on whether they included items related to general MHL or diagnosis-specific literacy. Existing definitions of MHL [ 12 , 13 , 14 , 15 , 28 ] were used to create a coding framework that clearly delineated its broad constituent domains (e.g. recognition, knowledge, stigma and beliefs), the object of these domains (e.g. mental illnesses, mental health prevention and promotion, and help-seeking), and their directionality (e.g. self vs. other) – see Fig.  1 .

MHL Coding Framework

Mental illness stigma was assessed using existing conceptualisation i.e. personal and perceived stigma relating to self (intra-personal) and others (inter-personal), and broad domains (e.g. attitudes and beliefs, emotional reactions, and social distancing) [ 45 ]. The coding of help-seeking beliefs was informed by the theory of planned behaviour [ 46 ], assessing not only help-seeking intentions but also help-seeking confidence and self-perceived help-seeking knowledge, perceived helpfulness of referrals, help-sources and treatments, help-seeking stigma and perceived help-seeking barriers. A distinction was also made between help-seeking beliefs for self (intra-personal) vs. others (inter-personal). Although not explicitly included in any MHL definition, help-seeking behaviour was also assessed as the term is sometimes confused with help-seeking intentions. Domains were coded at an item level due to many articles presenting this form of data (e.g. % of sample that answered each item correctly as opposed to a scale mean). Frequencies and percentages were produced across all articles and by study design and definition usage.

Assessment of measures

An assessment of all MHL related measurement tools was conducted in order to assess methodological homogeneity across articles, and whether there was evidence that the measures were psychometrically valid for adolescent samples. In order to present instruments with the most comprehensive psychometric assessments, measures were coded based on whether an article existed with the primary aim of establishing its psychometric properties with an adolescent sample.

Article selection and characteristics

In total, 206 articles were identified that presented extractable adolescent data on at least one MHL domain. Of these, 91 articles (44%) used the term ‘mental health literacy’. Those that did not use the term ( N  = 115, 56%), were therefore not perceived to have intended to explicitly measure the construct and were not included beyond this point. (see Fig.  2 . for a PRISMA flowchart of articles, Additional File 2 . for the full set of coded articles, and Additional File 3 . for the reference list of included articles).

PRISMA Flowchart of Included Studies

Synthesised findings

Design, context and aims.

Figure  3 shows the number of publications by year and country. Australian research dominated the field up until 2013, at which point there was an increase in research being published globally. Australia (34%), USA (15%), Canada (9%), Republic of Ireland (9%) and the UK (8%) have published the majority of research between 2003 and 2017.

Publication Count by Year and Country

Table  1 presents a summary of articles’ study design, context and primary aim. The majority of articles reported on school-based studies. Articles with the primary aim of describing levels of MHL also included variables such as age, school year, gender, education, socio-economic variables, occupation, urbanicity, mental health status and previous mental health service use.

• Conceptualisation

Of the 91 articles that used the term ‘mental health literacy’, only 41 (45%) defined it. The most common definition, presented by 29 out of 41 (71%) articles, was that coined by Jorm and colleagues [ 12 ]. A further 3 articles (7%) used a simplified or adapted version of this definition [ 47 , 48 , 49 ]. Four articles (10%) defined MHL as related to knowledge only (e.g. ‘knowledge of mental health problems as well as the sources of help available’ ; ( [ 50 ] pp. 485) . The full list of MHL domains presented by Jorm and colleagues [ 13 ], was included in over a third ( N  = 14, 34%) of articles that defined the term. However, there was some variation. For example, very few of these articles ( N  = 2, 14%) referred to different types of psychological distress as well as mental disorders when presenting the recognition domain. Furthermore, in most cases ( N  = 11, 79%), ‘knowledge and beliefs’ was replaced with ‘knowledge’ only, for domains relating to causes and risk factors, self-help strategies and professional help available.

A small number of articles that defined MHL ( N  = 5, 12%) presented Jorm’s additional domains relating to mental health first aid skills and advocacy [ 14 ]. Some articles ( N  = 4, 10%) provided examples of specific MHL domains, namely recognition of mental disorders and knowledge and beliefs about appropriate help-seeking and treatment, as opposed to presenting a comprehensive list. An emerging group of articles ( N  = 5, 12%) either acknowledged mental health promotion as a component of MHL or presented Kutcher and colleagues’ four broad domains including ‘understanding how to obtain and maintain good mental health’ ( [ 15 ] pp 155).

Regardless of whether a definition was provided, approximately one third of identified articles ( N  = 31, 34%) referred to MHL as a construct separate to mental illness stigma, with some suggesting that MHL predicts stigma. For example, articles described the measurement of these constructs as separate (e.g. ‘All respondents were then asked a series of questions that assessed sociodemographic characteristics, mental health literacy, stigma …’; ([ 51 ] pp. 941), and referred to or presented a relationship between the two constructs (e.g. ‘Participants with higher MHL displayed more negative attitudes to mental illness’ ; ( [ 52 ] pp. 100) . There were also instances where articles presented MHL as a predictor of help-seeking intentions and attitudes (e.g. ‘Studies indicate that in general, mental health literacy improves help seeking attitudes’ ; [ 53 ] (pp. 2), or used the term MHL to refer only to improved knowledge (e.g. ‘to assess the extent to which the students had learned the curriculum and developed what we called ‘depression literacy’ ; ([ 54 ] pp. 230).

• Measurement

Thirty-nine (43%) articles included items relating to general MHL. The exact terminology varied across studies e.g. mental disorder [ 55 ], mental illness [ 56 ], mental health problem [ 57 ], and mental health issue [ 58 ]. Few articles included items relating to mental health as opposed to mental ill-health. Bjørnsen et al. developed and validated a scale to assess adolescents' knowledge of how to obtain and maintain good mental health [ 28 ]. Kutcher et al. and McLuckie et al. also included an individual knowledge item that assessed an understanding of the complete mental health state (e.g. ‘People who have mental illness can at the same time have mental health’ ) [ 59 , 60 ].

Table  2 . presents the frequency and percentage of articles that assessed different types of diagnosis-specific literacy. In line with this focus, 57 (63%) articles utilized a vignette methodology, basing questions on descriptions, stories and scenarios relating to an individual meeting diagnostic criteria for a given mental disorder. Of these articles, 12 (21%) used comparator vignettes describing individuals with physical health problems (e.g. asthma or diabetes), control characters with good academic attainment, or ‘normal issues’ or mental health problems relating to stressful life events (e.g. the death of an elderly relative or the end of a romantic relationship). Table  3 . presents the frequency and percentage of articles that assessed different domains of MHL.

Measurement tools were too heterogeneous to conduct meta-analyses. As noted in Table 1 , four articles (4%) had the primary aim of validating MHL related measures with adolescent samples [ 28 , 55 , 61 , 62 ]. The scales assessed in Bjørnsen et al. and Pang et al. measured only one broad domain of MHL; knowledge of mental health promotion and mental illness stigma respectively [ 28 , 62 ]. Hart et al. assessed the psychometric properties of a depression knowledge questionnaire and found a one factor general knowledge latent structure to be the best fit to the data [ 61 ]. Campos et al. aimed to provide a more comprehensive assessment of MHL, and by psychometrically assessing a pool of items, developed a 33-item tool with three latent factors: first aid skills and help seeking, knowledge/stereotypes, and self-help strategies [ 55 ]. A further 22 articles (24%), stated that some items or scales had been developed for the purpose of the study.

Thirty-nine articles (43%) stated that they based their items on Jorm and colleagues original MHL survey or later 2006 and 2011 versions [ 12 , 63 ]. Furthermore, two articles (2%) included items from the Mental Health First Aid Questionnaire (MHFAQ) as detailed by Hart et al. [ 64 ]. However, there is no evidence of the validity of these surveys as whole scales, and researchers commonly selected and modified items. The Friend in Need Questionnaire, similar to Jorm and colleagues MHL survey in that it covers multiple MHL domains, was developed by Burns and Rapee to avoid leading multiple-choice answers. Instead, open-ended responses were coded in order to quantify levels of MHL [ 65 ]. Despite finding six articles (7%) that utilised a version of this questionnaire, no published validation paper was found. As part of the Adolescent Depression Awareness Programme (ADAP), an Adolescent Depression Knowledge Questionnaire (ADKQ) was developed and later validated [ 61 ]. Six articles (7%), including the validation paper, presented data using versions of the ADKQ.

Due to the multi-faceted nature of stigma, a range of measurement tools were identified across articles. The Attribution Questionnaire (AQ-27) was originally developed by Corrigan and colleagues [ 66 , 67 ] along with a brief 9-item scale (r-AQ) covering the following emotional reactions: blame, anger, pity, help, dangerousness, fear, avoidance, segregation and coercion. A similar 8-item version (AQ-8-C) was also developed for children [ 68 ]. The r-AQ was adapted by Watson et al. for use with middle school aged adolescents [ 69 ], and a 5-item version was more recently validated by Pinto et al. [ 70 ]. Four articles (4%) identified in this review used variations of the r-AQ.

Link et al. developed the 5-item Social Distance Scale (SDS) [ 71 ], which was later adapted for young people [ 72 ]. This version was more recently validated with a large sample aged 15–25 [ 73 ]. Five articles (5%) cited this version of the SDS. Seven articles (8%) used variations of the World Psychiatric Association’s (WPA) social distance items [ 74 ]; however, no adolescent validation paper was found. This review also found factual and attitudinal WPA scales presented by Pinfold et al. including the Myths and Facts About Schizophrenia Questionnaire. In total, these scales, or modified versions, were used in eight articles (9%), but no validation papers were found. The Reported and Intended Behaviour Scale (RIBS) [ 75 ] was utilised in three articles (3%). This scale has been translated into Japanese and Italian, and there is evidence of its validity with adult and university student samples [ 76 , 77 ]. The evidence of its validity with an adolescent sample was mixed [ 78 ].

The Depression Stigma Scale (DSS) was developed by Griffiths et al. to measure personal and perceived depression stigma [ 79 ]. Yap et al. later validated the DSS and confirmed that personal and perceived stigma were distinct constructs comprised of ‘weak-not-sick’ and ‘dangerous/unpredictable’ factors in a sample aged 15–25 [ 73 ]. Six articles (7%) utilised a version of the DSS, more commonly the items relating to personal stigma. Items from the Opinions about Mental Illness Scale (OMI) were used in two articles (2%). The original scale was cited by both [ 80 ], however, a Chinese version of the OMI has been tested for validity with a sample of secondary school students [ 81 ]. Other validated stigma scales identified included: the Attitudes Toward Serious Mental Illness Scale–Adolescent Version (ATSMI-AV) [ 82 ] ( N  = 1, 1%) and the Subjective Social Status Loss Scale [ 83 ] ( N  = 1, 1%). Measures of help-seeking attitudes and intentions were often not validated with adolescent samples. Two articles (2%) modified the General Help Seeking Questionnaire (GHSQ), previously validated for use with high school students [ 84 ]. A further two articles (2%) utilised the Self-Stigma of Seeking Help (SSOSH) scale; however, tests of its validity have only been conducted with college students [ 85 ].

The aims of this review were to investigate the conceptualisation and measurement of MHL in adolescent research, and scope the extent of methodological homogeneity for possible meta-analyses. The review clearly shows an increase in school-based MHL research with adolescent samples in recent years. This makes sense given that adolescence is increasingly identified as an important period for improving MHL and access to mental health services [ 6 , 10 , 11 , 38 ]. However, the field is still dominated by research from Western, developed countries and takes a predominantly mental-ill health approach. Furthermore, numerous challenges and inconsistencies have emerged in the field over the past 20 years.

Included articles were required to use the term ‘mental health literacy’ or a diagnosis-specific equivalent. However, by first including all articles that presented data for at least one MHL domain, a large number of articles that measured domains without referring to MHL were revealed. Researchers were measuring the same constructs but providing different labels indicating problems with discriminant validity [ 31 , 37 ]. It must be acknowledged that some of the articles included in the final set may have used the term without intending to measure the whole construct, and some articles were removed that measured multiple domains. For example, 16 intervention studies, previously included in a systematic literature review of the effectiveness of MHL interventions [ 25 ], were excluded from this current review because they did not use the term. Despite the exclusion of some potentially relevant data on a domain level, this criterion was considered most appropriate given one of the aims was to assess the conceptualisation of MHL.

Although under half of the articles identified defined MHL, those that did predominantly used definitions from Jorm and colleagues [ 12 , 13 , 14 ]. However, the various adaptations and interpretations of the original definition has clearly led to a lack of construct travelling in the field, in particular, confusion about the inclusion of beliefs and stigma related constructs as MHL domains. Furthermore, few articles referred to mental health and varying degrees of psychological distress in addition to mental illness, supporting the argument that current MHL definitions take a predominantly mental-ill health approach [ 16 , 26 ].

Although an adolescent specific definition of MHL may not be necessary, definitions frequently adopted by articles in this review were developed for adults. It is important for future research to consider not only cognitive development but also the unique social structures and vulnerabilities of adolescents in the conceptualisation and assessment of MHL. Given that the definition of adolescence in the current study ranges from 10 to 19 years, it is clear that even within this age range, different developmental factors could be considered. Applying integrated models of generic health literacy to MHL that acknowledge the life course and social and environmental determinants should therefore be a future priority [ 86 , 87 ].

Around a third of articles measured recognition of specific mental illnesses, with the majority using open-ended questions such as ‘ What, if anything, do you think is wrong …’, and calculating the % of correct responses. Knowledge of mental illnesses was measured more frequently than knowledge of prevention and promotion, therefore an understanding of the complete mental health state was often neglected [ 27 ]. More research is needed to develop and validate measures that assess the ability to seek out, comprehend, appraise and apply information relating to the complete mental health state as opposed to only assessing literacy of mental disorders. By using measurement tools that predominantly focus on psychiatric labels, there is evidence to suggest that stigma could be increased [ 22 , 23 ]. Given that over three quarters of intervention studies identified in this review included a measure of stigma, future research should consider the way in which mental-ill health approaches to MHL, in terms of intervention content and study measures, may influence stigma related outcomes.

It is perhaps unsurprising that the MHL field continues to be modelled on psychiatric labelling given the influence of Jorm and colleagues early work in Australia that came out of the National Health and Medical Research Council (NHMRC) Social Psychiatry Research Unit [ 12 ]. Kutcher and colleagues MHL definition also has its origins in psychiatry, but more explicitly includes understanding of mental health promotion and stigma reduction [ 15 ]. A growing body of research relating to eating disorders literacy also emphasises the need to distinguish between health promotion, prevention and early intervention initiatives in reducing the population health burden of eating-disordered behaviour and to prioritise mental health promotion programs, including those targeting stigma reduction [ 88 , 89 , 90 ]. This review identified an emerging group of articles that included understanding of how to obtain and maintain good mental health in their conceptualisation of MHL. However, this domain was rarely measured.

Just under half of the articles included items relating to general MHL. However, terminology was varied (e.g. mental illness, mental disorder, mental health problem, mental health issue). Leighton revealed that young people have a lack of conceptual clarity when it comes to these mental health related terms, unsurprising given the lack of consistent definitions in practice [ 91 ]. The range and subjectivity of mental health related terms reduces the meaningfulness of comparisons across MHL studies. Similarly, over half of the articles identified in this review assessed mental illness stigma, but the complexity of the construct caused heterogeneity in measurement. Intentions to seek help were the most commonly measured help-seeking belief; these findings support previous assessments of MHL measurement tools [ 16 ]. Measuring only intentions to seek help, without capturing knowledge of what help is available, will not provide a true picture of actual behaviour change. Findings also suggested that recognition and help-seeking related beliefs may be more directly associated with the MHL construct and, in line with previous literature [ 25 ], mental illness stigma was found to be a common outcome measure in MHL related interventions.

It is worth considering whether the MHL construct should continue to be stretched or whether we should accept that the multiple domains exist in their own right. For example, self-acquired knowledge and skills relating to positive psychology are being investigated, but are only just starting to emerge under the MHL construct [ 28 , 29 ]. Similarly, stigma and help-seeking knowledge and beliefs are assessed as part of, and independently from, the MHL framework. Adopting a multi-construct theory approach to MHL, as suggested by Spiker and Hammer [ 31 ], would see increased focus on developing and validating measures of specific MHL domains in order to better understand the way in which these domains relate to each other.

Developing better MHL theory will help provide clear logic models and theories of change for MHL interventions aiming to improve adolescent mental health, something currently lacking in the field. Although it should be acknowledged that the aims of MHL interventions will vary based on the scope, setting and cultural context, an increased number of validated measures as well as improved MHL theory could inform decisions about the most appropriate domain to measure as the outcome i.e. is the main aim of the intervention to reduce stigma or improve help-seeking. This is particularly important for school-based evaluations of MHL interventions for which respondent burden is often a concern.

We acknowledge that there were some articles in this review that adapted adult measures and tested for face and content validity with child and adolescent mental health professionals, and internal reliability and comprehension with adolescent samples. However, in general there was a lack of psychometric work to assess factor structure of scale-based measures in this age group, with large numbers of articles presenting data on an item level. More research should be conducted like that of Campos et al., working with young people to develop and psychometrically test pools of MHL items to identify latent factors [ 55 ]. This will help to inform future conceptualisation and measurement in this age group.

Even when there was evidence of a measure’s validity for use with adolescents, many articles selected only the items relevant for their study or adapted the scale to fit the cultural context. This may, in part, be an attempt to reduce the number of items and therefore the response burden. However, adaptation to measures based on the cultural discourse around mental health aligns with school-based mental health promotion approaches that account for children’s social, cultural and political contexts [ 92 ]. This raises the important question as to whether we should be trying to test and compare mental health related knowledge across cultures, particularly given the ongoing levels of disagreement amongst mental health professions between and within countries. A previous review of cross-cultural conceptualisations of positive mental health concluded that future definitions should be inclusive and culturally sensitive, and that more work was needed to empirically validate criteria for mental health [ 93 ]. Future research should consider conducting measurement invariance on existing MHL measures across different cultures. A comparison of knowledge items and their pre-defined correct answers, could help understand cultural differences in the discourse around mental health and what it means to be mental health literate across contexts.

Given the increased political interest in mental health promotion and education [ 6 , 38 ], we recommend that MHL research focuses on increasing understanding of ways to promote and maintain positive mental health, including subjective wellbeing, optimal functioning, coping and resilience [ 30 , 94 ]. Examples of knowledge items with true/false responses were identified in the current review and many aligned with a biogenetic conceptualisation of mental illness. Not only could these ‘truths’ cause more negative attitudes towards individuals experiencing mental health difficulties [ 19 ], many mapped directly onto the content of interventions and therefore do not provide any evidence of adolescents’ ability to critically appraise mental health information. To enhance individual and community level critical mental health literacy, the MHL field should apply models of public health literacy that aim to increase empowerment and control over health decisions, and acknowledge the interaction between an individual’s ability and their social and contextual demands [ 86 , 95 , 96 , 97 ]. Given that mental health is a key component of health, it is also worth questioning the usefulness of this separation moving forward; a MHL field that is playing catch up with more developed health literacy approaches could further exaggerate the existing lack of parity of esteem.

MHL research with adolescent populations is on the rise, but this review has highlighted some important areas for future consideration. Increasingly stretched definitions of MHL have led to conceptual confusion and methodological inconsistency, and there is a lack of measures developed and psychometrically tested with adolescents. Furthermore, the field is still dominated by a mental-ill health approach, with limited measures assessing the promotion of positive mental health. We suggest that the MHL field moves away from assessing ‘mental disorder literacy’ and towards critical ‘mental health literacy’. A better understanding of what MHL means for adolescents is needed in order to develop reliable, valid and feasible measures that acknowledge their developmental stage and unique social and contextual demands. In conclusion, by treating MHL as a multi-construct theory, more could be understood about the mechanisms for change in improving adolescent mental health.

Availability of data and materials

Link to PROSPERO review protocol included in the manuscript, example search strategy included as supplementary material.

Abbreviations

• Mental health literacy

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RM designed the systematic literature review and wrote the protocol published on PROSPERO. RM conducted the initial database search and grey literature search and was responsible for all stages of screening and data extraction. Any uncertainties relating to screening and data extraction were resolved through discussion with NH and PP. A sub-set of articles were screened at full text stage by NH to determine levels of agreement. RM wrote the first draft of the manuscript with input from NH and PP. All authors read and approved the submitted version.

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Mansfield, R., Patalay, P. & Humphrey, N. A systematic literature review of existing conceptualisation and measurement of mental health literacy in adolescent research: current challenges and inconsistencies. BMC Public Health 20 , 607 (2020). https://doi.org/10.1186/s12889-020-08734-1

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55 research questions about mental health

Last updated

11 March 2024

Reviewed by

Brittany Ferri, PhD, OTR/L

Research in the mental health space helps fill knowledge gaps and create a fuller picture for patients, healthcare professionals, and policymakers. Over time, these efforts result in better quality care and more accessible treatment options for those who need them.

Use this list of mental health research questions to kickstart your next project or assignment and give yourself the best chance of producing successful and fulfilling research.

• Why does mental health research matter?

Mental health research is an essential area of study. It includes any research that focuses on topics related to people’s mental and emotional well-being.

As a complex health topic that, despite the prevalence of mental health conditions, still has an unending number of unanswered questions, the need for thorough research into causes, triggers, and treatment options is clear.

Research into this heavily stigmatized and often misunderstood topic is needed to find better ways to support people struggling with mental health conditions. Understanding what causes them is another crucial area of study, as it enables individuals, companies, and policymakers to make well-informed choices that can help prevent illnesses like anxiety and depression.

• How to choose a strong mental health research topic

As one of the most important parts of beginning a new research project, picking a topic that is intriguing, unique, and in demand is a great way to get the best results from your efforts.

Mental health is a blanket term with many niches and specific areas to explore. But, no matter which direction you choose, follow the tips below to ensure you pick the right topic.

Prioritize your interests and skills

While a big part of research is exploring a new and exciting topic, this exploration is best done within a topic or niche in which you are interested and experienced.

Research is tough, even at the best of times. To combat fatigue and increase your chances of pushing through to the finish line, we recommend choosing a topic that aligns with your personal interests, training, or skill set.

Consider emerging trends

Topical and current research questions are hot commodities because they offer solutions and insights into culturally and socially relevant problems.

Depending on the scope and level of freedom you have with your upcoming research project, choosing a topic that’s trending in your area of study is one way to get support and funding (if you need it).

Not every study can be based on a cutting-edge topic, but this can be a great way to explore a new space and create baseline research data for future studies.

Assess your resources and timeline

Before choosing a super ambitious and exciting research topic, consider your project restrictions.

You’ll need to think about things like your research timeline, access to resources and funding, and expected project scope when deciding how broad your research topic will be. In most cases, it’s better to start small and focus on a specific area of study.

Broad research projects are expensive and labor and resource-intensive. They can take years or even decades to complete. Before biting off more than you can chew, consider your scope and find a research question that fits within it.

Read up on the latest research

Finally, once you have narrowed in on a specific topic, you need to read up on the latest studies and published research. A thorough research assessment is a great way to gain some background context on your chosen topic and stops you from repeating a study design. Using the existing work as your guide, you can explore more specific and niche questions to provide highly beneficial answers and insights.

• Trending research questions for post-secondary students

As a post-secondary student, finding interesting research questions that fit within the scope of your classes or resources can be challenging. But, with a little bit of effort and pre-planning, you can find unique mental health research topics that will meet your class or project requirements.

Examples of research topics for post-secondary students include the following:

How does school-related stress impact a person’s mental health?

To what extent does burnout impact mental health in medical students?

How does chronic school stress impact a student’s physical health?

How does exam season affect the severity of mental health symptoms?

Is mental health counseling effective for students in an acute mental crisis?

• Research questions about anxiety and depression

Anxiety and depression are two of the most commonly spoken about mental health conditions. You might assume that research about these conditions has already been exhausted or that it’s no longer in demand. That’s not the case at all.

According to a 2022 survey by Centers for Disease Control and Prevention (CDC), 12.5% of American adults struggle with regular feelings of worry, nervousness, and anxiety, and 5% struggle with regular feelings of depression. These percentages amount to millions of lives affected, meaning new research into these conditions is essential.

If either of these topics interests you, here are a few trending research questions you could consider:

Does gender play a role in the early diagnosis of anxiety?

How does untreated anxiety impact quality of life?

What are the most common symptoms of anxiety in working professionals aged 20–29?

To what extent do treatment delays impact quality of life in patients with undiagnosed anxiety?

To what extent does stigma affect the quality of care received by people with anxiety?

Here are some examples of research questions about depression:

Does diet play a role in the severity of depression symptoms?

Can people have a genetic predisposition to developing depression?

How common is depression in work-from-home employees?

Does mood journaling help manage depression symptoms?

What role does exercise play in the management of depression symptoms?

• Research questions about personality disorders

Personality disorders are complex mental health conditions tied to a person’s behaviors, sense of self, and how they interact with the world around them. Without a diagnosis and treatment, people with personality disorders are more likely to develop negative coping strategies during periods of stress and adversity, which can impact their quality of life and relationships.

There’s no shortage of specific research questions in this category. Here are some examples of research questions about personality disorders that you could explore:

What environments are more likely to trigger the development of a personality disorder?

What barriers impact access to care for people with personality disorders?

To what extent does undiagnosed borderline personality disorder impact a person’s ability to build relationships?

How does group therapy impact symptom severity in people with schizotypal personality disorder?

What is the treatment compliance rate of people with paranoid personality disorder?

• Research questions about substance use disorders

“Substance use disorders” is a blanket term for treatable behaviors and patterns within a person’s brain that lead them to become dependent on illicit drugs, alcohol, or prescription medications. It’s one of the most stigmatized mental health categories.

The severity of a person’s symptoms and how they impact their ability to participate in their regular daily life can vary significantly from person to person. But, even in less severe cases, people with a substance use disorder display some level of loss of control due to their need to use the substance they are dependent on.

This is an ever-evolving topic where research is in hot demand. Here are some example research questions:

To what extent do meditation practices help with craving management?

How effective are detox centers in treating acute substance use disorder?

Are there genetic factors that increase a person’s chances of developing a substance use disorder?

How prevalent are substance use disorders in immigrant populations?

To what extent do prescription medications play a role in developing substance use disorders?

• Research questions about mental health treatments

Treatments for mental health, pharmaceutical therapies in particular, are a common topic for research and exploration in this space.

Besides the clinical trials required for a drug to receive FDA approval, studies into the efficacy, risks, and patient experiences are essential to better understand mental health therapies.

These types of studies can easily become large in scope, but it’s possible to conduct small cohort research on mental health therapies that can provide helpful insights into the actual experiences of the people receiving these treatments.

Here are some questions you might consider:

What are the long-term effects of electroconvulsive therapy (ECT) for patients with severe depression?

How common is insomnia as a side effect of oral mental health medications?

What are the most common causes of non-compliance for mental health treatments?

How long does it take for patients to report noticeable changes in symptom severity after starting injectable mental health medications?

What issues are most common when weaning a patient off of an anxiety medication?

• Controversial mental health research questions

If you’re interested in exploring more cutting-edge research topics, you might consider one that’s “controversial.”

Depending on your own personal values, you might not think many of these topics are controversial. In the context of the research environment, this depends on the perspectives of your project lead and the desires of your sponsors. These topics may not align with the preferred subject matter.

That being said, that doesn’t make them any less worth exploring. In many cases, it makes them more worthwhile, as they encourage people to ask questions and think critically.

Here are just a few examples of “controversial” mental health research questions:

To what extent do financial crises impact mental health in young adults?

How have climate concerns impacted anxiety levels in young adults?

To what extent do psychotropic drugs help patients struggling with anxiety and depression?

To what extent does political reform impact the mental health of LGBTQ+ people?

What mental health supports should be available for the families of people who opt for medically assisted dying?

• Research questions about socioeconomic factors & mental health

Socioeconomic factors—like where a person grew up, their annual income, the communities they are exposed to, and the amount, type, and quality of mental health resources they have access to—significantly impact overall health.

This is a complex and multifaceted issue. Choosing a research question that addresses these topics can help researchers, experts, and policymakers provide more equitable and accessible care over time.

Examples of questions that tackle socioeconomic factors and mental health include the following:

How does sliding scale pricing for therapy increase retention rates?

What is the average cost to access acute mental health crisis care in [a specific region]?

To what extent does a person’s environment impact their risk of developing a mental health condition?

How does mental health stigma impact early detection of mental health conditions?

To what extent does discrimination affect the mental health of LGBTQ+ people?

• Research questions about the benefits of therapy

Therapy, whether that’s in groups or one-to-one sessions, is one of the most commonly utilized resources for managing mental health conditions. It can help support long-term healing and the development of coping mechanisms.

Yet, despite its popularity, more research is needed to properly understand its benefits and limitations.

Here are some therapy-based questions you could consider to inspire your own research:

In what instances does group therapy benefit people more than solo sessions?

How effective is cognitive behavioral therapy for patients with severe anxiety?

After how many therapy sessions do people report feeling a better sense of self?

Does including meditation reminders during therapy improve patient outcomes?

To what extent has virtual therapy improved access to mental health resources in rural areas?

• Research questions about mental health trends in teens

Adolescents are a particularly interesting group for mental health research due to the prevalence of early-onset mental health symptoms in this age group.

As a time of self-discovery and change, puberty brings plenty of stress, anxiety, and hardships, all of which can contribute to worsening mental health symptoms.

If you’re looking to learn more about how to support this age group with mental health, here are some examples of questions you could explore:

Does parenting style impact anxiety rates in teens?

How early should teenagers receive mental health treatment?

To what extent does cyberbullying impact adolescent mental health?

What are the most common harmful coping mechanisms explored by teens?

How have smartphones affected teenagers’ self-worth and sense of self?

• Research questions about social media and mental health

Social media platforms like TikTok, Instagram, YouTube, Facebook, and X (formerly Twitter) have significantly impacted day-to-day communication. However, despite their numerous benefits and uses, they have also become a significant source of stress, anxiety, and self-worth issues for those who use them.

These platforms have been around for a while now, but research on their impact is still in its infancy. Are you interested in building knowledge about this ever-changing topic? Here are some examples of social media research questions you could consider:

To what extent does TikTok’s mental health content impact people’s perception of their health?

How much non-professional mental health content is created on social media platforms?

How has social media content increased the likelihood of a teen self-identifying themselves with ADHD or autism?

To what extent do social media photoshopped images impact body image and self-worth?

Has social media access increased feelings of anxiety and dread in young adults?

• Mental health research is incredibly important

As you have seen, there are so many unique mental health research questions worth exploring. Which options are piquing your interest?

Whether you are a university student considering your next paper topic or a professional looking to explore a new area of study, mental health is an exciting and ever-changing area of research to get involved with.

Your research will be valuable, no matter how big or small. As a niche area of healthcare still shrouded in stigma, any insights you gain into new ways to support, treat, or identify mental health triggers and trends are a net positive for millions of people worldwide.

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Research Topics

Five research topics exploring the science of mental health.

Mental wellbeing is increasingly recognized as an essential aspect of our overall health. It supports our ability to handle challenges, build strong relationships, and live more fulfilling lives. The World Health Organization (WHO) emphasizes the importance of mental health by acknowledging it as a fundamental human right.

This Mental Health Awareness Week, we highlight the remarkable work of scientists driving open research that helps everyone achieve better mental health.

Here are five Research Topics that study themes including how we adapt to a changing world, the impact of loneliness on our wellbeing, and the connection between our diet and mental health.

All articles are openly available to view and download.

1 | Community Series in Mental Health Promotion and Protection, volume II

40.300 views | 16 articles

There is no health without mental health. Thus, this Research Topic collects ideas and research related to strategies that promote mental health across all disciplines. The goal is to raise awareness about mental health promotion and protection to ensure its incorporation in national mental health policies.

This topic is of relevance given the mental health crisis being experienced across the world right now. A reality that has prompted the WHO to declare that health is a state of complete physical, mental, and social wellbeing.

View Research Topic

2 | Dietary and Metabolic Approaches for Mental Health Conditions

176.800 views | 11 articles

There is increased recognition that mental health disorders are, at least in part, a form of diet-related disease. For this reason, we focus attention on a Research Topic that examines the mechanistic interplay between dietary patterns and mental health conditions.

There is a clear consensus that the quality, quantity, and even timing of our human feeding patterns directly impact how brains function. But despite the epidemiological and mechanistic links between mental health and diet-related diseases, these two are often perceived as separate medical issues.

Even more urgent, public health messaging and clinical treatments for mental health conditions place relatively little emphasis on formulating nutrition to ease the underlying drivers of mental health conditions.

3 | Comparing Mental Health Cross-Culturally

94.000 views | 15 articles

Although mental health has been widely discussed in later years, how mental health is perceived across different cultures remains to be examined. This Research Topic addresses this gap and deepens our knowledge of mental health by comparing positive and negative psychological constructs cross-culturally.

The definition and understanding of mental health remain to be refined, partially because of a lack of cross-cultural perspectives on mental health. Also, due to the rapid internationalization taking place in the world today, a culturally aware understanding of, and interventions for mental health problems are essential.

4 | Adaption to Change and Coping Strategies: New Resources for Mental Health

85.000 views | 29 articles

In this Research Topic, scientists study a wider range of variables involved in change and adaptation. They examine changes of any type or magnitude whenever the lack of adaptive response diminishes our development and well-being.

Today’s society is characterized by change, and sometimes, the constant changes are difficult to assimilate. This may be why feelings of frustration and defenselessness appear in the face of the impossibility of responding adequately to the requirements of a changing society.

Therefore, society must develop an updated notion of the processes inherent to changing developmental environments, personal skills, resources, and strategies. This know-how is crucial for achieving and maintaining balanced mental health.

5 | Mental Health Equity

29.900 views | 10 articles

The goal of this Research Topic is to move beyond a synthesis of what is already known about mental health in the context of health equity. Rather, the focus here is on transformative solutions, recommendations, and applied research that have real world implications on policy, practice, and future scholarship.

Attention in the field to upstream factors and the role of social and structural determinants of health in influencing health outcomes, combined with an influx of innovation –particularly the digitalization of healthcare—presents a unique opportunity to solve pressing issues in mental health through a health equity lens.

The topic is opportune because factors such as structural racism and climate change have disproportionately negatively impacted marginalized communities across the world, including Black, Indigenous, People of Color (BIPOC), LGBTQ+, people with disabilities, and transition-age youth and young adults. As a result, existing disparities in mental health have exacerbated.

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Impact of COVID-19 pandemic on mental health in the general population: A systematic review

Jiaqi xiong.

a Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON

Orly Lipsitz

c Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Ontario

Flora Nasri

Leanna m.w. lui, hartej gill, david chen-li, michelle iacobucci.

e Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore

f Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore

Amna Majeed

Roger s. mcintyre.

b Department of Psychiatry, University of Toronto, Toronto, Ontario

d Brain and Cognition Discovery Foundation, Toronto, ON

Associated Data

As a major virus outbreak in the 21st century, the Coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented hazards to mental health globally. While psychological support is being provided to patients and healthcare workers, the general public's mental health requires significant attention as well. This systematic review aims to synthesize extant literature that reports on the effects of COVID-19 on psychological outcomes of the general population and its associated risk factors.

A systematic search was conducted on PubMed, Embase, Medline, Web of Science, and Scopus from inception to 17 May 2020 following the PRISMA guidelines. A manual search on Google Scholar was performed to identify additional relevant studies. Articles were selected based on the predetermined eligibility criteria.

Results: Relatively high rates of symptoms of anxiety (6.33% to 50.9%), depression (14.6% to 48.3%), post-traumatic stress disorder (7% to 53.8%), psychological distress (34.43% to 38%), and stress (8.1% to 81.9%) are reported in the general population during the COVID-19 pandemic in China, Spain, Italy, Iran, the US, Turkey, Nepal, and Denmark. Risk factors associated with distress measures include female gender, younger age group (≤40 years), presence of chronic/psychiatric illnesses, unemployment, student status, and frequent exposure to social media/news concerning COVID-19.

Limitations

A significant degree of heterogeneity was noted across studies.

Conclusions

The COVID-19 pandemic is associated with highly significant levels of psychological distress that, in many cases, would meet the threshold for clinical relevance. Mitigating the hazardous effects of COVID-19 on mental health is an international public health priority.

1. Introduction

In December 2019, a cluster of atypical cases of pneumonia was reported in Wuhan, China, which was later designated as Coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO) on 11 Feb 2020 ( Anand et al., 2020 ). The causative virus, SARS-CoV-2, was identified as a novel strain of coronaviruses that shares 79% genetic similarity with SARS-CoV from the 2003 SARS outbreak ( Anand et al., 2020 ). On 11 Mar 2020, the WHO declared the outbreak a global pandemic ( Anand et al., 2020 ).

The rapidly evolving situation has drastically altered people's lives, as well as multiple aspects of the global, public, and private economy. Declines in tourism, aviation, agriculture, and the finance industry owing to the COVID-19 outbreak are reported as massive reductions in both supply and demand aspects of the economy were mandated by governments internationally ( Nicola et al., 2020 ). The uncertainties and fears associated with the virus outbreak, along with mass lockdowns and economic recession are predicted to lead to increases in suicide as well as mental disorders associated with suicide. For example, McIntyre and Lee (2020b) have reported a projected increase in suicide from 418 to 2114 in Canadian suicide cases associated with joblessness. The foregoing result (i.e., rising trajectory of suicide) was also reported in the USA, Pakistan, India, France, Germany, and Italy ( Mamun and Ullah, 2020 ; Thakur and Jain, 2020 ). Separate lines of research have also reported an increase in psychological distress in the general population, persons with pre-existing mental disorders, as well as in healthcare workers ( Hao et al., 2020 ; Tan et al., 2020 ; Wang et al., 2020b ). Taken together, there is an urgent call for more attention given to public mental health and policies to assist people through this challenging time.

The objective of this systematic review is to summarize extant literature that reported on the prevalence of symptoms of depression, anxiety, PTSD, and other forms of psychological distress in the general population during the COVID-19 pandemic. An additional objective was to identify factors that are associated with psychological distress.

Methods and results were formated based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( Moher et al., 2010 ).

2.1. Search strategy

A systematic search following the PRISMA 2009 flow diagram ( Fig. 1 ) was conducted on PubMed, Medline, Embase, Scopus, and Web of Science from inception to 17 May 2020. A manual search on Google Scholar was performed to identify additional relevant studies. The search terms that were used were: (COVID-19 OR SARS-CoV-2 OR Severe acute respiratory syndrome coronavirus 2 OR 2019nCoV OR HCoV-19) AND (Mental health OR Psychological health OR Depression OR Anxiety OR PTSD OR PTSS OR Post-traumatic stress disorder OR Post-traumatic stress symptoms) AND (General population OR general public OR Public OR community). An example of search procedure was included as a supplementary file.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) study selection flow diagram. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

2.2. Study selection and eligibility criteria

Titles and abstracts of each publication were screened for relevance. Full-text articles were accessed for eligibility after the initial screening. Studies were eligible for inclusion if they: 1) followed cross-sectional study design; 2) assessed the mental health status of the general population/public during the COVID-19 pandemic and its associated risk factors; 3) utilized standardized and validated scales for measurement. Studies were excluded if they: 1) were not written in English or Chinese; 2) focused on particular subgroups of the population (e.g., healthcare workers, college students, or pregnant women); 3) were not peer-reviewed; 4) did not have full-text availability.

2.3. Data extraction

A data extraction form was used to include relevant data: (1) Lead author and year of publication, (2) Country/region of the population studied, (3) Study design, (4) Sample size, (5) Sample characteristics, (6) Assessment tools, (7) Prevalence of symptoms of depression/anxiety/ PTSD/psychological distress/stress, (8) Associated risk factors.

2.4 Quality appraisal

The Newcastle-Ottawa Scale (NOS) adapted for cross-sectional studies was used for study quality appraisal, which was modified accordingly from the scale used in Epstein et al. (2018) . The scale consists of three dimensions: Selection, Comparability, and Outcome. There are seven categories in total, which assess the representativeness of the sample, sample size justification, comparability between respondents and non-respondents, ascertainments of exposure, comparability based on study design or analysis, assessment of the outcome, and appropriateness of statistical analysis. A list of specific questions was attached as a supplementary file. A total of nine stars can be awarded if the study meets certain criteria, with a maximum of four stars assigned for the selection dimension, a maximum of two stars assigned for the comparability dimension, and a maximum of three stars assigned for the outcome dimension.

3.1. Search results

In total, 648 publications were identified. Of those, 264 were removed after initial screening due to duplication. 343 articles were excluded based on the screening of titles and abstracts. 41 full-text articles were assessed for eligibility. There were 12 articles excluded for studying specific subgroups of the population, five articles excluded for not having a standardized/ appropriate measure, three articles excluded for being review papers, and two articles excluded for being duplicates. Following the full-text screening, 19 studies met the inclusion criteria.

3.2. Study characteristics

Study characteristics and primary study findings are summarized in Table 1 . The sample size of the 19 studies ranged from 263 to 52,730 participants, with a total of 93,569 participants. A majority of study participants were over 18 years old. Female participants ( n  = 60,006) made up 64.1% of the total sample. All studies followed a cross-sectional study design. The 19 studies were conducted in eight different countries, including China ( n  = 10), Spain ( n  = 2), Italy ( n  = 2), Iran ( n  = 1), the US ( n  = 1), Turkey ( n  = 1), Nepal ( n  = 1), and Denmark ( n  = 1). The primary outcomes chosen in the included studies varied across studies. Twelve studies included measures of depressive symptoms while eleven studies included measures of anxiety. Symptoms of PTSD/psychological impact of events were evaluated in four studies while three studies assessed psychological distress. It was additionally observed that four studies contained general measures of stress. Three studies did not explicitly report the overall prevalence rates of symptoms; notwithstanding the associated risk factors were identified and discussed.

Summary of study sample characteristics, study design, assessment tools used, prevalence rates and associated risk factors.

3.3. Quality appraisal

The result of the study quality appraisal is presented in Table 2 . The overall quality of the included studies is moderate, with total stars awarded varying from four to eight. There were two studies with four stars, two studies with five stars, seven studies with six stars, seven studies with seven stars, and one study with eight stars.

Results of study quality appraisal of the included studies.

3.4. Measurement tools

A variety of scales were used in the studies ( n  = 19) for assessing different adverse psychological outcomes. The Beck Depression Inventory-II (BDI-II), Patient Health Questionnaire-9/2 (PHQ-9/2), Self-rating Depression Scales (SDS), The World Health Organization-Five Well-Being Index (WHO-5), and Center for Epidemiologic Studies Depression Scale (CES-D) were used for measuring depressive symptoms. The Beck Anxiety Inventory (BAI), Generalized Anxiety Disorder 7/2-item (GAD-7/2), and Self-rating Anxiety Scale (SAS) were used to evaluate symptoms of anxiety. The Depression, Anxiety, and Stress Scale- 21 items (DASS-21) was used for the evaluation of depression, anxiety, and stress symptoms. The Hospital Anxiety and Depression Scale (HADS) was used for assessing anxiety and depressive symptoms. Psychological distress was measured by The Peritraumatic Distress Inventory (CPDI) and the Kessler Psychological Distress Scale (K6/10). Symptoms of PTSD were assessed by The Impact of Event Scale-(Revised) (IES(-R)), PTSD Checklist (PCL-(C)-2/5). Chinese Perceived Stress Scale (CPSS-10) was used in one study to evaluate symptoms of stress.

3.5. Symptoms of depression and associated risk factors

Symptoms of depression were assessed in 12 out of the 19 studies ( Ahmed et al., 2020 ; Gao et al., 2020 ; González-Sanguino et al., 2020 ; Huang and Zhao, 2020 ; Lei et al., 2020 ; Mazza et al., 2020 ; Olagoke et al., 2020 ; Ozamiz-Etxebarria et al., 2020 ; Özdin and S.B. Özdin, 2020 ; Sønderskov et al., 2020 ; Wang et al., 2020a ; Wang et al., 2020b ). The prevalence of depressive symptoms ranged from 14.6% to 48.3%. Although the reported rates are higher than previously estimated one-year prevalence (3.6% and 7.2%) of depression among the population prior to the pandemic ( Huang et al., 2019 ; Lim et al., 2018 ), it is important to note that presence of depressive symptoms does not reflect a clinical diagnosis of depression.

Many risk factors were identified to be associated with symptoms of depression amongst the COVID-19 pandemic. Females were reported as are generally more likely to develop depressive symptoms when compared to their male counterparts ( Lei et al., 2020 ; Mazza et al., 2020 ; Sønderskov et al., 2020 ; Wang et al., 2020a ). Participants from the younger age group (≤40 years) presented with more depressive symptoms ( Ahmed et al., 2020 ; Gao et al., 2020 ; Huang and Zhao, 2020 ; Lei et al., 2020 ; Olagoke et al., 2020 ; Ozamiz-Etxebarria et al., 2020 ;). Student status was also found to be a significant risk factor for developing more depressive symptoms as compared to other occupational statuses (i.e. employment or retirement) ( González et al., 2020 ; Lei et al., 2020 ; Olagoke et al., 2020 ). Four studies also identified lower education levels as an associated factor with greater depressive symptoms ( Gao et al., 2020 ; Mazza et al., 2020 ; Olagoke et al., 2020 ; Wang et al., 2020a ). A single study by Wang et al., 2020b reported that people with higher education and professional jobs exhibited more depressive symptoms in comparison to less educated individuals and those in service or enterprise industries.

Other predictive factors for symptoms of depression included living in urban areas, poor self-rated health, high loneliness, being divorced/widowed, being single, lower household income, quarantine status, worry about being infected, property damage, unemployment, not having a child, a past history of mental stress or medical problems, having an acquaintance infected with COVID-19, perceived risks of unemployment, exposure to COVID-19 related news, higher perceived vulnerability, lower self-efficacy to protect themselves, the presence of chronic diseases, and the presence of specific physical symptoms ( Gao et al., 2020 ; González-Sanguino et al., 2020 ; Lei et al., 2020 ; Mazza et al., 2020 ; Olagoke et al., 2020 ; Ozamiz-Etxebarria et al., 2020 ; Özdin and Özdin, 2020 ; Wang et al., 2020a ).

3.6. Symptoms of anxiety and associated risk factors

Anxiety symptoms were assessed in 11 out of the 19 studies, with a noticeable variation in the prevalence of anxiety symptoms ranging from 6.33% to 50.9% ( Ahmed et al., 2020 ; Gao et al., 2020 ; González-Sanguino et al., 2020 ; Huang and Zhao, 2020 ; Lei et al., 2020 ; Mazza et al., 2020 ; Moghanibashi-Mansourieh, 2020 ; Ozamiz-Etxebarria et al., 2020 ; Özdin and Özdin, 2020 ; Wang et al., 2020a ; Wang et al., 2020b ).

Anxiety is often comorbid with depression ( Choi et al., 2020 ). Some predictive factors for depressive symptoms also apply to symptoms of anxiety, including a younger age group (≤40 years), lower education levels, poor self-rated health, high loneliness, female gender, divorced/widowed status, quarantine status, worry about being infected, property damage, history of mental health issue/medical problems, presence of chronic illness, living in urban areas, and the presence of specific physical symptoms ( Ahmed et al., 2020 ; Gao et al., 2020 ; González-Sanguino et al., 2020 ; Huang and Zhao, 2020 ; Lei et al., 2020 ; Mazza et al., 2020 ;  Moghanibashi-Mansourieh, 2020 ; Ozamiz-Etxebarria et al., 2020 ; Ozamiz-Etxebarria et al., 2020 ; Wang et al., 2020a ; Wang et al., 2020b ).

Additionally, social media exposure or frequent exposure to news/information concerning COVID-19 was positively associated with symptoms of anxiety ( Gao et al., 2020 ; Moghanibashi-Mansourieh, 2020 ). With respect to marital status, one study reported that married participants had higher levels of anxiety when compared to unmarried participants ( Gao et al., 2020 ). On the other hand, Lei et al. (2020) found that divorced/widowed participants developed more anxiety symptoms than single or married individuals. A prolonged period of quarantine was also correlated with higher risks of anxiety symptoms. Intuitively, contact history with COVID-positive patients or objects may lead to more anxiety symptoms, which is noted in one study ( Moghanibashi-Mansourieh, 2020 ).

3.7. Symptoms of PTSD/ psychological distress/stress and associated risk factors

With respect to PTSD symptoms, similar prevalence rates were reported by Zhang and Ma (2020) and N. Liu et al. (2020) at 7.6% and 7%, respectively. Despite using the same measurement scale as Zhang and Ma (2020) (i.e., IES), Wang et al. (2020a) noted a remarkably different result, with 53.8% of the participants reporting moderate-to-severe psychological impact. González et al. ( González-Sanguino et al., 2020 ) noted 15.8% of participants with PTSD symptoms. Three out of the four studies that measured the traumatic effects of COVID-19 reported that the female gender was more susceptible to develop symptoms of PTSD. In contrast, the research conducted by Zhang and Ma (2020) found no significant difference in IES scores between females and males. Other risk factors included loneliness, individuals currently residing in Wuhan or those who have been to Wuhan in the past several weeks (the hardest-hit city in China), individuals with higher susceptibility to the virus, poor sleep quality, student status, poor self-rated health, and the presence of specific physical symptoms. Besides sex, Zhang and Ma (2020) found that age, BMI, and education levels are also not correlated with IES-scores.

Non-specific psychological distress was also assessed in three studies. One study reported a prevalence rate of symptoms of psychological distress at 38% ( Moccia et al., 2020 ), while another study from Qiu et al. (2020) reported a prevalence of 34.43%. The study from Wang et al. (2020) did not explicitly state the prevalence rates, but the associated risk factors for higher psychological distress symptoms were reported (i.e., younger age groups and female gender are more likely to develop psychological distress) ( Qiu et al., 2020 ; Wang et al., 2020 ). Other predictive factors included being migrant workers, profound regional severity of the outbreak, unmarried status, the history of visiting Wuhan in the past month, higher self-perceived impacts of the epidemic ( Qiu et al., 2020 ; Wang et al., 2020 ). Interestingly, researchers have identified personality traits to be predictive of psychological distresses. For example, persons with negative coping styles, cyclothymic, depressive, and anxious temperaments exhibit greater susceptibility to psychological outcomes ( Wang et al., 2020 ; Moccia et al., 2020 ).

The intensity of overall stress was evaluated and reported in four studies. The prevalence of overall stress was variably reported between 8.1% to over 81.9% ( Wang et al., 2020a ; Samadarshi et al., 2020 ; Mazza et al., 2020 ). Females and the younger age group are often associated with higher stress levels as compared to males and the elderly. Other predictive factors of higher stress levels include student status, a higher number of lockdown days, unemployment, having to go out to work, having an acquaintance infected with the virus, presence of chronic illnesses, poor self-rated health, and presence of specific physical symptoms ( Wang et al., 2020a ; Samadarshi et al., 2020 ; Mazza et al., 2020 ).

3.8. A separate analysis of negative psychological outcomes

Out of the nineteen included studies, five studies appeared to be more representative of the general population based on the results of study quality appraisal ( Table 1 ). A separate analysis was conducted for a more generalizable conclusion. According to the results of these studies, the rates of negative psychological outcomes were moderate but higher than usual, with anxiety symptoms ranging from 6.33% to 18.7%, depressive symptoms ranging from 14.6% to 32.8%, stress symptoms being 27.2%, and symptoms of PTSD being approximately 7% ( Lei et al., 2020 ; Liu et al., 2020 ; Mazza et al., 2020 ; Wang et al., 2020b ; Zhang et al., 2020 ). In these studies, female gender, younger age group (≤40 years), and student population were repetitively reported to exhibit more adverse psychiatric symptoms.

3.9. Protective factors against symptoms of mental disorders

In addition to associated risk factors, a few studies also identified factors that protect individuals against symptoms of psychological illnesses during the pandemic. Timely dissemination of updated and accurate COVID-19 related health information from authorities was found to be associated with lower levels of anxiety, stress, and depressive symptoms in the general public ( Wang et al., 2020a ). Additionally, actively carrying out precautionary measures that lower the risk of infection, such as frequent handwashing, mask-wearing, and less contact with people also predicted lower psychological distress levels during the pandemic ( Wang et al., 2020a ). Some personality traits were shown to correlate with positive psychological outcomes. Individuals with positive coping styles, secure and avoidant attachment styles usually presented fewer symptoms of anxiety and stress ( Wang et al., 2020 ; Moccia et al., 2020 ). ( Zhang et al. 2020 ) also found that participants with more social support and time to rest during the pandemic exhibited lower stress levels.

4. Discussion

Our review explored the mental health status of the general population and its predictive factors amid the COVID-19 pandemic. Generally, there is a higher prevalence of symptoms of adverse psychiatric outcomes among the public when compared to the prevalence before the pandemic ( Huang et al., 2019 ; Lim et al., 2018 ). Variations in prevalence rates across studies were noticed, which could have resulted from various measurement scales, differential reporting patterns, and possibly international/cultural differences. For example, some studies reported any participants with scores above the cut-off point (mild-to-severe symptoms), while others only included participants with moderate-to-severe symptoms ( Moghanibashi-Mansourieh, 2020 ; Wang et al., 2020a ). Regional differences existed with respect to the general public's psychological health during a massive disease outbreak due to varying degrees of outbreak severity, national economy, government preparedness, availability of medical supplies/ facilities, and proper dissemination of COVID-related information. Additionally, the stage of the outbreak in each region also affected the psychological responses of the public. Symptoms of adverse psychological outcomes were more commonly seen at the beginning of the outbreak when individuals were challenged by mandatory quarantine, unexpected unemployment, and uncertainty associated with the outbreak ( Ho et al., 2020 ). When evaluating the psychological impacts incurred by the coronavirus outbreak, the duration of psychiatric symptoms should also be taken into consideration since acute psychological responses to stressful or traumatic events are sometimes protective and of evolutionary importance ( Yaribeygi et al., 2017 ; Brosschot et al., 2016 ; Gilbert, 2006 ). Being anxious and stressed about the outbreak mobilizes people and forces them to implement preventative measures to protect themselves. Follow-up studies after the pandemic may be needed to assess the long-term psychological impacts of the COVID-19 pandemic.

4.1. Populations with greater susceptibility

Several predictive factors were identified from the studies. For example, females tended to be more vulnerable to develop the symptoms of various forms of mental disorders during the pandemic, including depression, anxiety, PTSD, and stress, as reported in our included studies ( Ahmed et al., 2020 ; Gao et al., 2020 ; Lei et al., 2020 ). Greater psychological distress arose in women partially because they represent a higher percentage of the workforce that may be negatively affected by COVID-19, such as retail, service industry, and healthcare. In addition to the disproportionate effects that disruption in the employment sector has had on women, several lines of research also indicate that women exhibit differential neurobiological responses when exposed to stressors, perhaps providing the basis for the overall higher rate of select mental disorders in women ( Goel et al., 2014 ; Eid et al., 2019 ).

Individuals under 40 years old also exhibited more adverse psychological symptoms during the pandemic ( Ahmed et al., 2020 ; Gao et al., 2020 ; Huang and Zhao, 2020 ). This finding may in part be due to their caregiving role in families (i.e., especially women), who provide financial and emotional support to children or the elderly. Job loss and unpredictability caused by the COVID-19 pandemic among this age group could be particularly stressful. Also, a large proportion of individuals under 40 years old consists of students who may also experience more emotional distress due to school closures, cancelation of social events, lower study efficiency with remote online courses, and postponements of exams ( Cao et al., 2020 ). This is consistent with our findings that student status was associated with higher levels of depressive symptoms and PTSD symptoms during the COVID-19 outbreak ( Lei et al., 2020 ; Olagoke et al., 2020 , Wang et al., 2020a ; Samadarshi et al., 2020 ).

People with chronic diseases and a history of medical/ psychiatric illnesses showed more symptoms of anxiety and stress ( Mazza et al., 2020 ; Ozamiz-Etxebarria et al., 2020 ; Özdin and Özdin, 2020 ). The anxiety and distress of chronic disease sufferers towards the coronavirus infection partly stem from their compromised immunity caused by pre-existing conditions, which renders them susceptible to the infection and a higher risk of mortality, such as those with systemic lupus erythematosus ( Sawalha et al., 2020 ). Several reports also suggested that a substantially higher death rate was noted in patients with diabetes, hypertension and other coronary heart diseases, yet the exact causes remain unknown ( Guo et al., 2020 ; Emami et al., 2020 ), leaving those with these common chronic conditions in fear and uncertainty. Additionally, another practical aspect of concern for patients with pre-existing conditions would be postponement and inaccessibility to medical services and treatment as a result of the COVID-19 pandemic. For example, as a rapidly growing number of COVID-19 patients were utilizing hospital and medical resources, primary, secondary, and tertiary prevention of other diseases may have unintentionally been affected. Individuals with a history of mental disorders or current diagnoses of psychiatric illnesses are also generally more sensitive to external stressors, such as social isolation associated with the pandemic ( Ho et al., 2020 ).

4.2. COVID-19 related psychological stressors

Several studies identified frequent exposure to social media/news relating to COVID-19 as a cause of anxiety and stress symptoms ( Gao et al., 2020 ; Moghanibashi-Mansourieh, 2020 ). Frequent social media use exposes oneself to potential fake news/reports/disinformation and the possibility for amplified anxiety. With the unpredictable situation and a lot of unknowns about the novel coronavirus, misinformation and fake news are being easily spread via social media platforms ( Erku et al., 2020 ), creating unnecessary fears and anxiety. Sadness and anxious feelings could also arise when constantly seeing members of the community suffering from the pandemic via social media platforms or news reports ( Li et al., 2020 ).

Reports also suggested that poor economic status, lower education level, and unemployment are significant risk factors for developing symptoms of mental disorders, especially depressive symptoms during the pandemic period ( Gao et al., 2020 ; Lei et al., 2020 ; Mazza et al., 2020 ; Olagoke et al., 2020 ;). The coronavirus outbreak has led to strictly imposed stay-home-order and a decrease in demands for services and goods ( Nicola et al., 2020 ), which has adversely influenced local businesses and industries worldwide. Surges in unemployment rates were noted in many countries ( Statistics Canada, 2020 ; Statista, 2020 ). A decrease in quality of life and uncertainty as a result of financial hardship can put individuals into greater risks for developing adverse psychological symptoms ( Ng et al., 2013 ).

4.3. Efforts to reduce symptoms of mental disorders

4.3.1. policymaking.

The associated risk and protective factors shed light on policy enactment in an attempt to relieve the psychological impacts of the COVID-19 pandemic on the general public. Firstly, more attention and assistance should be prioritized to the aforementioned vulnerable groups of the population, such as the female gender, people from age group ≤40, college students, and those suffering from chronic/psychiatric illnesses. Secondly, governments must ensure the proper and timely dissemination of COVID-19 related information. For example, validation of news/reports concerning the pandemic is essential to prevent panic from rumours and false information. Information about preventative measures should also be continuously updated by health authorities to reassure those who are afraid of being infected ( Tran, et al., 2020a ). Thirdly, easily accessible mental health services are critical during the period of prolonged quarantine, especially for those who are in urgent need of psychological support and individuals who reside in rural areas ( Tran et al., 2020b ). Since in-person health services are limited and delayed as a result of COVID-19 pandemic, remote mental health services can be delivered in the form of online consultation and hotlines ( Liu et al., 2020 ; Pisciotta et al., 2019 ). Last but not least, monetary support (e.g. beneficial funds, wage subsidy) and new employment opportunities could be provided to people who are experiencing financial hardship or loss of jobs owing to the pandemic. Government intervention in the form of financial provisions, housing support, access to psychiatric first aid, and encouragement at the individual level of healthy lifestyle behavior has been shown effective in alleviating suicide cases associated with economic recession ( McIntyre and Lee, 2020a ). For instance, declines in suicide incidence were observed to be associated with government expenses in Japan during the 2008 economic depression ( McIntyre and Lee, 2020a ).

4.3.2. Individual efforts

Individuals can also take initiatives to relieve their symptoms of psychological distress. For instance, exercising regularly and maintaining a healthy diet pattern have been demonstrated to effectively ease and prevent symptoms of depression or stress ( Carek et al., 2011 ; Molendijk et al., 2018 ; Lassale et al., 2019 ). With respect to pandemic-induced symptoms of anxiety, it is also recommended to distract oneself from checking COVID-19 related news to avoid potential false reports and contagious negativity. It is also essential to obtain COVID-19 related information from authorized news agencies and organizations and to seek medical advice only from properly trained healthcare professionals. Keeping in touch with friends and family by phone calls or video calls during quarantine can ease the distress from social isolation ( Hwang et al., 2020 ).

4.4. Strengths

Our paper is the first systematic review that examines and summarizes existing literature with relevance to the psychological health of the general population during the COVID-19 outbreak and highlights important associated risk factors to provide suggestions for addressing the mental health crisis amid the global pandemic.

4.5. Limitations

Certain limitations apply to this review. Firstly, the description of the study findings was qualitative and narrative. A more objective systematic review could not be conducted to examine the prevalence of each psychological outcome due to a high heterogeneity across studies in the assessment tools used and primary outcomes measured. Secondly, all included studies followed a cross-sectional study design and, as such, causal inferences could not be made. Additionally, all studies were conducted via online questionnaires independently by the study participants, which raises two concerns: 1] Individual responses in self-assessment vary in objectivity when supervision from a professional psychiatrist/ interviewer is absent, 2] People with poor internet accessibility were likely not included in the study, creating a selection bias in the population studied. Another concern is the over-representation of females in most studies. Selection bias and over-representation of particular groups indicate that most studies may not be representative of the true population. Importantly, studies in inclusion were conducted in a limited number of countries. Thus generalizations of mental health among the general population at a global level should be made cautiously.

5. Conclusion

This systematic review examined the psychological status of the general public during the COVID-19 pandemic and stressed the associated risk factors. A high prevalence of adverse psychiatric symptoms was reported in most studies. The COVID-19 pandemic represents an unprecedented threat to mental health in high, middle, and low-income countries. In addition to flattening the curve of viral transmission, priority needs to be given to the prevention of mental disorders (e.g. major depressive disorder, PTSD, as well as suicide). A combination of government policy that integrates viral risk mitigation with provisions to alleviate hazards to mental health is urgently needed.

Authorship contribution statement

JX contributed to the overall design, article selection , review, and manuscript preparation. LL and JX contributed to study quality appraisal. All other authors contributed to review, editing, and submission.

Declaration of Competing Interest

Acknowledgements.

RSM has received research grant support from the Stanley Medical Research Institute and the Canadian Institutes of Health Research/Global Alliance for Chronic Diseases/National Natural Science Foundation of China and speaker/consultation fees from Lundbeck, Janssen, Shire, Purdue, Pfizer, Otsuka, Allergan, Takeda, Neurocrine, Sunovion, and Minerva.

Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.jad.2020.08.001 .

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