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Abstract

BACKGROUND:

While mental illness (e.g., depression, anxiety) has been examined frequently in the workplace, the COVID-19 pandemic has only increased the attention towards mental illness. Mental well-being views mental health as a continuum from ill health to thriving. Few studies have examined factors associated with mental well-being in the workplace. Public stigma for mental illness, the general population’s negative attitude towards mental illness, and occupational burnout are examined.

OBJECTIVE:

The purpose of this study was to examine the relationship between burnout and public stigma on mental well-being in a sample of employees across industries in the United States.

METHODS:

Employees surveyed from 16 companies from various industries were assessed. Room Here, a non-profit organization whose goal is to improve employee mental fitness, gathered data from these companies located in the western mountain region of the U.S. Data were collected during the pandemic. Across this portfolio of companies, 259 employees were included in the sample. Survey assesses respondents’ mental well-being, stigma towards mental illness, and occupational burnout. Ordinary least squares (OLS) regression was used in this cross-sectional study on secondary data.

RESULTS:

Results suggested occupational burnout was associated with a decrease in mental well-being, while public stigma was associated with an improvement in mental well-being.

CONCLUSION:

This study sought to examine the relationship between mental well-being, burnout, and public stigma. Employee burnout and public stigma were related to mental well-being. Implications for burnout and stigma reduction programs are discussed as well as future research.

Keywords

Company wellness mental health COVID-19 stigma burnout employees non-profit

1 Introduction

Mental health is increasingly an important topic in the workplace [1]. In fact, 71% of employees believe their employer is more concerned about their employee’s mental health now than in the past and 81% reported they will be looking for workplaces that support mental health when seeking for future job opportunities [1]. Since the pandemic, employees have reported worsened mental health [2]. Distress levels quadrupled from 2018 to 2020 [3]. Employees in workplaces such as healthcare have reported increased mental illness related symptoms [4, 5] leading to resignation and early retirement [6]. This has impacted organizations negatively by increasing work absence [7 –11] and greatly impacting work productivity and functioning [12]. Studies of mental health have skewed heavily toward a focus on mental illness while overlooking the role of mental well-being [13] and its corresponding predictors. This study investigated the relationship between an employee’s mental well-being and their public stigma (the general population’s negative attitude towards mental illness) and occupational burnout (increased exhaustion and disengagement from employees primarily due to occupational stressors [14, 15]).

Mental well-being has been defined by the World Health Organization (WHO) as “not merely the absence of disease or infirmity, but rather, a state of complete physical, mental, and social well-being” [16]. Keyes postulates that mental well-being is a general sense of one’s mental flourishing across emotional, psychological, and social dimensions [13, 17]. Within the literature, there has been confusion between the use of the terms mental illness and mental well-being. While similar, mental illness and mental well-being are distinct from one another [18]. Mental illness and mental well-being are not opposite ends of the same continuum, rather, they each have their own continuum [13]. Mental illness ranges from severe mental illness to no mental illness, and well-being ranges from languishing to flourishing [17]. Keyes further [13] points out that it is possible to be free from a mental illness and not have optimal mental well-being. It is also possible to have mental illness, yet flourishing mental well-being.

While diagnosable mental illnesses such as depression and anxiety have been examined in the workplace [19, 20], few studies have looked at mental well-being within the workplace [21]. Among the current well-being research, adults with the highest levels of mental well-being in the U.S. have reported the fewest missed days of work, the fewest half-day work cutbacks, the healthiest psychosocial functioning and the fewest health limitations of daily activities [13]. Among the British population, a correlation was found between emotional well-being, a component of mental well-being, and innovation [22]. A negative correlation between mental well-being and absenteeism was found among Brazilian employees [23].

Similarly, even before the COVID-19 pandemic, scholars and practitioners have raised increased concern and attention regarding the phenomenon of “workplace burnout.” Workplace burnout is a specific form of stress driven primarily by occupational stressors [14] that affects individuals through both attitudinal (e.g., disengagement, cynicism) and energy (e.g., exhaustion) channels [24]. Burnout is not uncommon in the workplace. The majority of workers report experiencing burnout symptoms at least some of the time [25] and it has been found to develop in workers irrespective of their particular occupation [15]. Nevertheless, research has linked burnout to increased risk of mental illness, including insomnia, depressive symptoms, the use of psychotropic and antidepressant medications, and hospitalization for mental disorders and other psychological ill-health [26], which can impact organizational productivity and unnecessarily increase company costs [27].

In addition to burnout, public stigma –the general population’s negative attitude towards mental illness [28]–is associated with mental illness in those stigmatized [29]. Negative effects are worsened in the workplace as employees are led to fear that they will be treated differently and that their careers will be negatively impacted if they were to disclose their mental illness challenges. As such, employees conceal their mental illness concerns and try to manage on their own [30]. The greater the perceived discrimination, the greater the anxiety and depression experienced and as such the perception of experienced stigma has been shown to positively influence anxiety and depression [29]. In addition, stigma against mental illness serves as a barrier to seeking services [31], which possibly contributes to an increase in psychological illness [32].

Across the United States, public stigma towards mental illness is rampant [33] and promotes the devaluation of others [30]. Employees may perceive their occupation to be at risk for discussing mental illness with employment stakeholders [34, 35]. Public stigma of mental illness has been studied relatively little in the occupation setting and has been considered an underestimated contributing factor towards unemployment and worsened mental illness [35]. This study seeks to contribute to the literature by examining the relationship between stigma and burnout on mental well-being. This study will explore whether the relationships previously found in the literature showing a relationship between burnout and stigma with mental illness will also occur with mental well-being. Specifically, examining the possible relationship of the degree to which a person espouses stigma toward mental illness and experiencing occupational burnout with their mental well-being. While there has been much research on the relationship between stigma and mental illness, we are unaware of any studies examining the relationship of public mental illness stigma and its correlation with employee mental well-being. Furthermore, research typically examines the impact stigma has on the stigmatized not the potential “stigmatizer”.

We argue that employee burnout poses risk not only to employee mental illness, but also to employee mental well-being. As burned out employees experience exhaustion, we expect that they will have fewer cognitive and emotional resources available to dedicate to activities that support their mental well-being. Furthermore, we expect that the attitudinal nature of burnout (e.g., disengagement, cynicism) will interfere with employees’ ability to experience the subjective meaningfulness inherent in concepts of mental well-being [36]. On this basis, we predict that higher burnout will correlate with lower mental well-being.

We wonder if harboring thoughts, feelings, and behaviors with a negative valence towards a ubiquitous group. Thus, we hypothesize more held stigma would lead to lower mental wellbeing for the perceiver. This is based on the social-cognitive model which suggests that stigma is comprised of cognitive (stereotypes), affective (prejudice), and behavioral (discrimination) elements [37]. Furthermore, other forms of discrimination have been found to have negative effects on workplace culture which then negatively affect employees’ sense of psychological safety [38]. A decrease in psychological safety could decrease mental well-being.

Although burnout and public stigma related to mental illness have been examined separately in the workplace in the past [14, 35], recent research has shown a significant association between one another among workplace staff [39]. The aforementioned study found the greater stigma employees had, the greater occupational burnout they experienced. Stigma and burnout are prevalent in the workplace [25, 33] and have detrimental effects on employees and companies [26 , 30].

2 Methods

This secondary data, cross-sectional study examines the mental well-being of employees as it relates to their occupational burnout and stigma towards mental illness. The data used in this study were gathered by a non-profit organization, Room Here, as part of their consultation services. The institutional IRB deemed this study exempt from human subjects research.

2.1 Room here

Room Here is a non-profit organization whose vision is to “champion mental fitness at work to create healthier individuals, families, and communities” [40]. Room Here partners with companies who take a pledge to improve the mental well-being of their workplace. Room Here works with companies to create tailored mental well-being plans. Each company makes a commitment to improve mental well-being, articulates goals, receives broad education on processes for developing mental well-being plans as needed, and then gains access to resources to implement mental well-being plans. Room Here also administers a small battery of measures bi-annually and sends the results to enrolled companies. The results are intended to help companies assess salient mental well-being outcomes as they implement their mental well-being plans.

2.2 Participants

In this study, we examined a portfolio of 16 companies which represent industries including events, financial services, legal, medical, public relations, real estate, and technology. These 16 companies were either involved with Room Here’s consulting services or opted to have the survey completed. Data were collected from December 2020 to October 2021. This period of time occurred during the COVID pandemic. COVID had wide-reaching impacts on the workforce and workplace [41]. Processes, workflows, and norms were changed to respond to various government mandates and best practices to ensure employee safety. December 2020 [42] marked the emergency authorization of the Pfizer and Moderna vaccines in the United States, a turning point in the pandemic. The data collection window offers a view of employee mental well-being during unprecedented external stressors.

2.3 Measures

The Room Here employee survey assesses respondents’ mental well-being, stigma towards mental illness, and occupational burnout. The employee survey is anonymous and is distributed through Olumo, a survey platform. The first section of the survey gathers employee demographics. Then employees complete the Mental Health Continuum Short-Form (mental well-being) [43], the Attitudes about Mental Illness and its Treatment (stigma) [44], and the Oldenberg Burnout Inventory (burnout) [24].

2.4 Outcome variable

The outcome variable for this study is employee mental well-being as captured by the Mental Health Continuum Short-Form (MHC-SF) [43]. The MHC-SF is a 14-item self-report questionnaire with three subscales: emotional well-being, psychological well-being, and social well-being. The MHC-SF asks respondents to endorse how often they have felt each item (e.g., happy, interested in life) in the past month. Response options are a six-level Likert scale (never, once or twice, about once a week, about 2 or 3 times a week, almost every day, or every day). The scores are summed with a range of 0 to 70 with higher numbers indicating greater flourishing. MHC-SF places mental well-being on a continuum rather than viewing a mental illness as present or not. This expands our view of what being “well” is.

The MHC-SF psychometrics have been verified by several studies in various cultural contexts [43, 45]. A 38-country study found the MHC-SF to be a reliable and valid measure of mental well-being [46]. Reliability across studies in various countries show Cronbach’s alpha between .86 and .92 for the total scale, .75 to .92 for the emotional well-being subscale, .81 to .86 for the psychological well-being subscale, and .70 to .83 for the social well-being subscale [21].

2.5 Predictor variables

2.5.1 Stigma

Room Here measures mental illness related stigma using the Attitudes about Mental Illness and its Treatment scale (AMIT) [44]. This measure is a generic scale that was developed as a public surveillance tool to assess mental illness associated stigma [44]. This pragmatic self-report measure includes 11 statements and has two subscales: negative stereotypes and recovery. Employees endorse their level of agreement with each statement using a five-level Likert scale (strongly disagree, disagree, neither agree/disagree, agree, or strongly agree). Developers designed the instrument to be used in general public surveys and tested it on a representative sample of approximately 5,000 adults in the U.S. [44]. The Cronbach’s alpha for the negative stereotypes scale ranged from .69 to .70 and .66 to .69 for the recovery and outcomes subscale [44]. Room Here selected this measure because it has been used on the general public and is not condition specific. Many stigma instruments ask respondents to consider a specific mental illness. The AMIT looks at stigma more broadly.

2.5.2 Burnout

Room Here uses the Oldenburg Burnout Inventory (OLBI) to measure employee occupational burnout [24]. The OLBI is a self-report questionnaire with 16 items. Respondents are asked to indicate their level of agreement with statements (e.g., “I always find new and interesting aspects in my work”) using a 4-point Likert scale (strongly agree, agree, disagree, strongly disagree). The questionnaire’s two subscales are exhaustion and disengagement from work. Higher scores refer to higher levels of exhaustion and disengagement. Four items each in both subscales are reversed scored. The OLBI is psychometrically validated and has been used by various studies to explore burnout related phenomena in the workplace. The Cronbach’s alpha for the English version of the instrument ranges from α = .74 to .87 [47].

2.5.3 Covariate variables

Room Here gathered basic demographic information from the employees. We have included demographic variables in our model that have been used by other related empirical studies [13 , 45]. Room Here queries employees about their highest level of school completed [13, 45] (high school, some college, associate’s degree, bachelor’s degree, some graduate school, master’s degree, PhD/law/medical, other advanced degree), ethnicity [32, 45] (white non-Hispanic, Black/African American, Asian American, Hispanic or Latinx, American Indian/Native American/Alaska Native/Native Hawaiian, Pacific Islander multi-ethnic, self-describe, prefer not to say), and sexual orientation [34] (heterosexual, homosexual, bisexual, self-describe, prefer not to say).

2.6 Analysis

An ordinary least squares (OLS) regression (Equation 1) investigated the relationship of burnout and stigma with the sum score of the MHC-SF. The normal distribution of the outcome variable made OLS a suitable approach and fit the model by estimating the best fitting parameters for burnout and stigma given covariates.

Equation 1: $\begin{matrix} \hat{Y} = β_{0} + β_{Stigma} X_{Stigma} + β_{Burnout} X_{Burnout} \\ + β_{c} X_{c} + e \end{matrix}$

Where: $\hat{Y}$ = predicted mental well-being score

β₀ = y-intercept β_Stigma = stigma coefficient

β_Burnout = burnout coefficient

β_c = coefficients for vector of covariates c

e = error

A sensitivity analysis tested whether a mixed effects linear regression that allowed for between-company as well as between-person variation provided better fit to the data (Equation 2). Intra-class correlation (ICC) was calculated as the percentage total variation explained by level 2 (between-company) variation, with values of greater than 0.30 justifying use of random effects to account for clustering.

Equation 2: $\begin{matrix} {\hat{Y}}_{ij} = γ_{00} + u_{0 j} + γ_{10} + γ_{20} Stigm a_{ij} \\ + γ_{30} Burnou t_{ij} + γ_{40} Covariate s_{ij} + e_{ij} \end{matrix}$

Where: ${\hat{Y}}_{ij}$ –predicted mental well-being score for the i^th employee at the j^th company

γ_n0 = mean estimate for the n^th parameter

u_0j = random effect for each company

(allowing intercept to vary by company)

Model fit was assessed using R-squared values indicating the amount of variance in mental well-being scores explained by the model; nested models were compared using Akaike Information Criterion (AIC) values where lower values indicated better fit.

3 Results

3.1 Descriptive statistics

The descriptive statistics of the sample are detailed in Table 1. The sample for this study included 259 employees across 16 different companies. Individuals in the sample are mostly white (80%), most have at least a bachelor’s degree (67%), and few (17%) identify as a sexual minority. With a possible score ranging from 0 to 70, the average mental health continuum score was 44.60. The burnout score averages fell beneath the neutral cutoff point, indicating low levels of disengagement (2.57) and exhaustion (2.75), overall. Similarly, the stigma averages fell beneath the neutral cutoff point, suggesting low levels of negative stereotypes (2.12) and negative beliefs towards recovery (2.02).

Table 1
Sample demographics

M (SD) N (%)

Mental Well-being Score 44.60 (12.24)

Burnout

Disengagement 2.57 (0.65)

Exhaustion 2.75 (0.64)

Stigma

Negative stereotypes 2.12 (0.62)

Attitudes toward recovery 2.02 (0.49)

Ethnic Minority 52 (20.08%)

Highest Level of Education

High school degree 49 (18.92%)

Associate’s degree 37 (14.29%)

Bachelor’s degree 124 (47.88%)

Master’s degree 39 (15.06%)

PhD/MD/JD 10 (3.86%)

Sexual Minority 43 (16.60%)

	M (SD)	N (%)
Mental Well-being Score	44.60 (12.24)
Burnout
Disengagement	2.57 (0.65)
Exhaustion	2.75 (0.64)
Stigma
Negative stereotypes	2.12 (0.62)
Attitudes toward recovery	2.02 (0.49)
Ethnic Minority		52 (20.08%)
Highest Level of Education
High school degree		49 (18.92%)
Associate’s degree		37 (14.29%)
Bachelor’s degree		124 (47.88%)
Master’s degree		39 (15.06%)
PhD/MD/JD		10 (3.86%)
Sexual Minority		43 (16.60%)

3.2 OLS results

Results of the fixed and mixed effects models are presented in Table 2. The disengagement subscale for burnout was associated with a 3-point reduction in predicted mental well-being score (β=-2.83, p < 0.05). The “exhaustion” subscale for burnout was associated with an 8-point reduction in predicted mental well-being score (β=-7.83, p < 0.001). The “negative stereotypes” subscale for stigma was associated with a greater than 3-point increase in mental well-being scores (β=3.41, p < 0.001). The only statistically significant control was education level; having a master’s degree was associated with a 5-point increase in mental well-being scores compared to having just a high school diploma (β=5.36, p < 0.05).

Table 2
Results of linear regression predicting mental well-being outcomes

Model 1 Model 2

β SE β SE

Fixed Effects

Intercept 65.38* 4.70 65.38* 4.70

Burnout

Disengagement –2.83* 1.37 -2.83* 1.37

Exhaustion –7.83* 1.38 -7.38* 1.38

Stigma

Negative stereotypes 3.41 1.09 3.41 1.09

Attitudes toward recovery –1.22 1.33 -1.22 1.33

Ethnic Minority –3.13 1.70 -1.53 2.08

Highest Level of Education (ref = HS diploma)

Associate’s degree 4.32 2.26 4.32 2.26

Bachelor’s degree 3.23 1.76 3.23 1.76

Master’s degree 5.36* 2.26 5.36* 2.26

PhD/MD/JD 3.27 3.59 3.27 3.59

Sexual Minority –2.56 1.80 -2.56 1.80

Random Effects Variance SD Variance SD

Company Mean 0.0 0.0

Residual 109.10 10.45

Model Fit AIC R² AIC R²

1,956.71 0.31 1,930.21 0.31

	Model 1	Model 2
Fixed Effects
Intercept	65.38***	4.70	65.38***	4.70
Burnout
Disengagement	–2.83*	1.37	-2.83*	1.37
Exhaustion	–7.83***	1.38	-7.38***	1.38
Stigma
Negative stereotypes	3.41**	1.09	3.41**	1.09
Attitudes toward recovery	–1.22	1.33	-1.22	1.33
Ethnic Minority	–3.13	1.70	-1.53	2.08
Highest Level of Education (ref = HS diploma)
Associate’s degree	4.32	2.26	4.32	2.26
Bachelor’s degree	3.23	1.76	3.23	1.76
Master’s degree	5.36*	2.26	5.36*	2.26
PhD/MD/JD	3.27	3.59	3.27	3.59
Sexual Minority	–2.56	1.80	-2.56	1.80
Random Effects	Variance	SD	Variance	SD
Company Mean			0.0	0.0
Residual			109.10	10.45
Model Fit	AIC	R²	AIC	R²
	1,956.71	0.31	1,930.21	0.31

*p ≤.05; **p ≤.01; ***p ≤.001. Model 1 = Ordinary least squares regression. Model 2 = Two-level mixed effects model allowing variation between companies.

The ICC was 0.003, suggesting that very little total variation in mental well-being scores was accounted for by company-level clustering, rendering use of a mixed effects model unnecessary. A random-intercept model was nonetheless run as a sensitivity analysis given the theoretical rationale of company-level variation. Covariate values and statistical significance were nearly identical to the fixed effects model, with variance at the company level rounding to 0.0. Both models had the same R-squared values; although AIC was slightly lower for the random effects model, the fixed effects model was selected based on lack of statistical justification for random effects and for the sake of parsimony.

4 Discussion

The purpose of this study was to explore the mental well-being of employees and factors influencing their mental well-being. Specifically, we examined the potential influence of burnout and public stigma on mental well-being. These data were collected during the pandemic, a time of unique, broadbased stressors. We found a lower overall mental well-being compared to other samples [48 –50]. Higher burnout was associated with lower mental well-being and stigma was associated with higher mental well-being. This cross-industry study of the relationships between burnout, stigma, and mental well-being contributes to the literature in three ways. First, few studies have examined the mental well-being of employees in the U.S. [45]. Second, whereas studies have examined the role of burnout on mental illness [51], few have examined its relationship with the broader construct of mental well-being [24]. Third, to our knowledge this would be the first study to examine the role that public mental illness stigma plays on an individual’s mental well-being. Most research conducted has conflated mental illness and mental well-being [52, 53]. This creates difficulty identifying predictors for mental well-being. By identifying key potential predictors of mental well-being, employers and researchers could identify targets to improve employee productivity through increased mental well-being [13, 22]. Workplace practitioners and researchers seek to prevent illness, injury, and disability among employees and/or intervene as needed. By identifying factors associated with mental well-being, these practitioners and researchers can begin to develop programming to enhance the mental well-being of employees in targeted areas.

4.1 Mental well-being

Our sample’s mental well-being score, generally, was lower than the mental well-being scores of other samples in the literature. Direct comparisons with our sample are difficult given its cross-industry composition and geographic location. There are some proximal comparison groups. For example, one study consisting of professionals like bankers, health care professionals, telecom officers, and consultants in Pakistan found higher well-being scores [48]. In addition, a group of Portuguese professional psychologists reported higher well-being [50] and a large study among young adults in a U. S. college reported higher well-being [49] compared to our study’s sample.

Mental well-being is multifactorial, and the cross-sectional design limits our ability to disentangle causal inference. Of note, these data were gathered during the COVID-19 pandemic and other researchers have found that overall mental health has worsened [2]. This might partially explain the lower mental well-being score.

4.2 Burnout

Overall burnout results were consistent with averages in the United States across industries. A national multi-industry study found averages like ours [47]. Comparatively, employees outside the U.S. have reported less burnout. For example, a study examining burnout in a South African construction company found lower burnout scores [24]. One reason for this might be that a large portion of our sample work in the tech industry. This type of work is associated with increases in stress and burnout [54]. In addition, due to COVID-19, employees experienced higher distress levels and lacked social connections [2].

Our hypothesis was supported in that the higher burnout, the lower the mental well-being. This pattern occurred across both burnout subscales. We found the higher the respondents’ exhaustion, a component of burnout, the lower their mental well-being. Business professionals in human resources [55] and health care [56] have shown a similar relationship. Other studies in the literature have assessed the relationship with mental illness and burnout. They found that burnout increases depression, anxiety, psychological disorders and suicide [56]. The OLBI measure examines exhaustion as both cognitive and physical exhaustion. Stress, a form of cognitive exhaustion [57], has been shown to have a negative relationship with mental health in many organizational settings [58]. In addition, these results are in line with work previously based on the Job Demands-Resources model [15]. The less exhaustion experienced from work demands, the more motivation, higher job performance, and well-being employees experience [15, 59]. They also experience increases in engagement, self-efficacy, and self-esteem [60].

Higher disengagement, another component of burnout, correlated with lower mental well-being. Few studies have examined engagement’s influence on employee well-being across industries [61]. Studies examining U.S. social workers and Finnish dentists reveal less employee engagement is associated with lower physical and psychological well-being [61]. This fits with previous literature employee engagement and burnout have a relationship [62], and this relationship could influence well-being [56].

4.3 Stigma

Overall stigma in our study was below U.S. averages [44]. As mentioned earlier, this could be influenced in part by the COVID-19 pandemic. During the pandemic, rates of distress has exponentially increased [3]. As such, more individuals have experienced mental illness or know someone who has experienced a mental illness. Having a personal connection with mental illness reduces stigma [44].

Counter to our hypothesis, the greater the stigma the greater the mental well-being. The Negative Stereotypes scale had a significant, positive correlation with mental well-being. This finding was surprising because we expected perceived public stigma would have a negative association with the perceiver’s mental well-being. There is a dearth of research exploring the association between experienced public stigma and individual mental well-being, so it is difficult to understand and disentangle this finding. More frequently, the research focuses on the impact of public stigma on mental illness [29]. Our study sought to understand if holding bias would impact a person’s mental well-being.

We see two possible explanations for these unexpected results: First, social desirability response bias may have affected our results. Stigma measures capture the degree to which respondents view a phenomenon as undesirable. Respondents who view mental illness with greater stigma may have inflated their own self-ratings to ascribe to themselves attributes that they consider more socially acceptable. A second possibility is that those who are “flourishing” and report greater well-being may have had less personal experience with mental illness experiences, providing them with fewer opportunities to destigmatize and gain knowledge on mental illness challenges [63]. Additional research is needed to positively explain either of these scenarios.

4.4 Practical implications

A strength of this paper is its ecological validity, the relation between real-world phenomena and the investigation of these phenomena [64]. Data were collected from real workers on their actual experiences from companies across multiple industries assessing their mental well-being, stigma, and burnout at a given point in time. This study contributes to the literature as most studies have assessed mental illness symptoms rather than capturing a more comprehensive picture across an individual’s emotional, psychological, and social life [13, 17].

Millions of adults are employed full-time in the United States. Experiences at work can influence physical and mental well-being, and experiences outside of work can influence an employees’ functioning at work [26, 65]. Improving the mental well-being of the workforce would have population-level impacts. As such, it would require the joint effort of multiple stakeholders [66]. Employers, public health workers, and policy makers could collaborate to strengthen public surveillance, prevention, and intervention efforts within the workplace. For example, brief, routine surveys could be used in the workplace and data shared with public health offices to improve surveillance efforts. Public mental health workers could partner with workplaces to provide mental health prevention programming and referrals for those employees who need more intensive support.

4.5 Limitations

Our study has a number of limitations: the sample was not randomized, only a small number of companies participated, we only have data from a single timepoint, and we used secondary data. The study used a convenience sample gathered through a non-profit organization. Convenience sampling can lead to biased data collection and a lack of generalizability [67]. Similarly, inclusion/exclusion criteria were not used. Rather all the companies that agreed to participate with Room Here were included. Patino and Ferreria (2018) [68] suggest inclusion and exclusion criteria influence external validity and generalizability, findings may not be generalizable to all companies and workers. Variable selection was also limited in the data gathered by the non-profit which could lead to residual confounding. We did control for education, sexual minority, and ethnic minority; however, there may be other variables that were not available in the dataset that may explain some of these relationships. For example, future work may look at the role of additional variables, such as prior mental illness [20], and the use of control variables whose domains include other sociodemographic factors (e.g., income), as well as psychosocial exposures and workplace characteristics (e.g., hours worked). For example, one recent study investigated the effort-reward imbalance (ERI) model amongst nurses. The authors found that when nurses exercised increased effort with little perceived reward, the more burned out they reported to be [69]. This evidence suggests effort and perceived reward may moderate or mediate the association between mental well-being and burnout. There may also be potential information bias held by the non-profit who gathered the data. Future research could look at other industries and companies and their mental well-being.

Another limitation is our cross-sectional design. Stigma has been found to be a rather unstable concept and changes throughout an individual’s development due to transitioning environments such as education, employment, and social settings [70]. Due to this study capturing a single-time point, less is clear on its relationship with mental well-being over time. Future research would help assess stigma with mental well-being by conducting a longitudinal designed study because stigma may change over time. In addition, we are unable to draw cause-and-effect conclusions based on this study alone, due to the inability to separate the preceding concepts of stigma and burnout to the measured outcome, mental well-being [71].

Furthermore, response rates reduced the number of demographic variables we could use. Our data was gathered during COVID which limits generalizability to other time points. Our measure of mental well-being was retrospective in nature, real-time experience-sampling measures may be less affected by recall bias. Missing and low response data can ultimately negatively influence the statistical power in analysis and produce biased estimates, resulting invalid conclusions [72]. Future research should examine mental well-being and its predictors in the workplace using longitudinal designs with a larger sample [73].

5 Conclusion

This study aimed to examine the relationship mental well-being has with burnout and public stigma about mental illness. Consistent with our hypothesis, the greater the burnout an employee experiences, the lower their overall mental well-being. Contrary to our hypothesis, the greater the stigma an employee has, the greater their reported mental well-being. These findings lead to a few implications. First, previous research has found a relationship between burnout and mental illness. The greater the burnout, the greater the mental illness. Our study adds that mental well-being is also impacted by burnout. Hence, efforts to reduce burnout among employees has a two-fold benefit. Burnout reduction programs can help to reduce the effects of mental illness and it increases the employees’ mental well-being. Second, this study provides initial evidence that more strongly held stigma for mental illnesses may have a relationship with an employee’s well-being. Stigma reduction programming has typically been viewed as a positive use of time and resources for companies [31, 74]. Further research is needed to understand what impact stigma reduction efforts might have on mental well-being. Third, this study underscores the importance of viewing mental illness and mental well-being as separate constructs. While some factors may influence both, others may not. Future research is needed to compare both mental illness and mental well-being constructs.

Footnotes

Acknowledgments

We recognize Olumo for their service and support in data collection.

Ethical approval

The Brigham Young University Institutional Review Board determined that this study was not human subject’s research.

Informed consent

Not applicable.

Conflict of interest

Austin Fannin, Cole Hooley, and Cody J. Reeves –provided unpaid consultation support to Room Here. Katherine Marçal –none declared. Rachel Treglown and Rachel Woerner –were employees at Room Here. Rachel Treglown was a Co-Founder and Rachel Woerner was Director of Operations.

Funding

Cole Hooley received intramural funding from a Brigham Young University, Family, Home, and Social Science Dean’s grant. Room Here received funding from a Utah Community Builders Hope grant.

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Occupational burnout and public stigma associated with employee mental well-being: A multi-industry,cross-sectional study during COVID-19

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1 Introduction

2 Methods

2.1 Room here

2.2 Participants

2.3 Measures

2.4 Outcome variable

2.5 Predictor variables

2.5.1 Stigma

2.5.2 Burnout

2.5.3 Covariate variables

2.6 Analysis

3 Results

3.1 Descriptive statistics

4.1 Mental well-being

4.2 Burnout

4.3 Stigma

4.4 Practical implications

4.5 Limitations

5 Conclusion

Footnotes

Acknowledgments

Ethical approval

Informed consent

Conflict of interest

Funding

References