Background

While employment is a positive social determinant of mental health1 and crucial for achieving sustainable development goals2, the increased risk of common mental disorders from work-related psychosocial stressors is a growing concern for researchers and policymakers3. Working populations disproportionately reported common mental health symptoms, including psychological distress, anxiety, and depression symptoms4,5. Healthcare is a demanding occupation that increases the risk of mental health disorders including psychological distress, anxiety, and depression6. Assessment of mental health symptoms from a cause-specific perspective, and identification of work-related psychosocial factors may contribute to the current understanding and development of preventive strategies for mental health disorders, despite the numerous debates surrounding the causes and measures of mental health disorders. Such viewpoints align with recommendations from the earliest biopsychosocial health model for mental diseases7 and contemporary explanatory suggestions8. Therefore, our study determined the magnitude of common mental symptoms (CMSs) (distress, anxiety, and depression) from cause-specific perspectives and their determinants among Ethiopian healthcare workers.

Globally, studies conducted during the coronavirus pandemic (COVID-19) have revealed that a significant proportion of healthcare workers experience persistent distress (37%), anxiety (40%), and depression (37%)9. Studies from high-income countries show that the health profession is ranked in terms of work-related anxiety10, third in terms of depression11, and first in terms of other helping professionals12. In the Ethiopian context, studies during the COVID-19 era reported a mean prevalence ranging from 12.4 to 61.9% for distress, 21.9–78% for anxiety, and 20.2–60.3% for depressive symptoms13,14,15,16 despite no study before the pandemic. Prevalence during such adverse situations and specific contexts appears to overestimate the prevalence, which may not inform policymakers of the existing problem among healthcare workers. In addition, studies thus far have measured common mental symptoms (stress, anxiety, and depression) from a stressor-neutral perspective or non-work-ascribed approach. This may fail to provide conclusive evidence for policymakers and researchers. Pieces of evidence also present conflicting information regarding the risk classification of healthcare occupations, with some studies reporting them as high-risk occupations17, and others reporting low-risk occupations18.

To date, occupational stress19,20 has been measured using non-cause specific measures such as perceived stress scale (PSS) or selecting elements of life stress from a patient health questionnaire (PHQ) or depression, anxiety, and stress scale (DASS)21 to quantify the problem. In other words, “non-cause specific” measures cannot be used to determine whether work-related stressors or other life-related stressors cause psychological distress. The nonwork-ascribed measures do not refer to the term “occupational stress” unless cause-specific or work-ascribed perspectives are considered. Therefore, measures that address at least one type of work-related psychosocial stress may help to capture work-related stressors’ reactions, and distress. This also helps reduce the underestimation or overestimation of the prevalence of occupational stress and provides clear information for policymakers and implementers on which they should invest their resources to prevent, control, and identify and treat chronic mental health symptoms early.

Similarly, studies on depressive symptoms in healthcare workers in Ethiopia have used only “non-cause specific” measures such as the patient health questionnaire (PHQ)21healthcare workers. This can mislead the estimation of work-ascribed depression, and its determinants for reasons similar to those explained by occupational stress. Recently, scholars have suggested the need for a paradigm shift to measure and quantify the prevalence of depressive symptoms22,23,24 in a cause-specific or work-ascribed manner to supplement the previous cause-neutral approaches. This may contribute more to whether the depressive symptoms are caused by specific work-related psychosocial stressors. The same analogy works for symptoms of generalized anxiety. In addition, studies from Ethiopia13,14,15,16 on depression, anxiety and mental distress among healthcare workers reported higher proportions, possibly as a result of the perceived adversity of the pandemic. Therefore, the prevalence of adversity may not also show a consistent magnitude of mental health disorders resulting in a lack of generalizability to interventions for healthcare workers’ mental well-being. Therefore, the authors believe that determining the proportions of healthcare workers with those mental health symptoms and their determinants in cause-specific measures would provide additional evidence to the workplace mental health policymakers.

Keeping measurement differences in mind, studies thus far have shown that depressive symptoms in healthcare workers are more common among females13 those with a history of disease or injury14, and those with low job satisfaction25. Studies show that depressive symptoms in healthcare workers are more common among females, those with a history of disease or injury, and those with low job satisfaction. Despite the unavailability of studies involving Ethiopian workers, sleep disorders such as insomnia26,27,28,29, and life-threatening events (LTEs) increase the risk of depression30,31, and working hours increase the risk of depression symptoms29,32, which could also increase the risk of depressive symptoms in healthcare workers. Studies that measured work-related generalized anxiety among Ethiopian healthcare workers healthcare workers are limited. However, studies from high-income countries that measured generalized anxiety via a non-cause specific approach showed that poor perceived health, job dissatisfaction among nurses33,34, forms of harassment such as a history of verbal violence6, higher sleep disorder scores35, and (LTE)31 scores increase the odds of reporting anxiety symptoms in healthcare workers. Similarly, regarding the educational level of medical specialities14, behavioural factors such as consuming coffee (a source of caffeine)36 at bedtime (3–6 h of bedtime) increased the odds of reporting occupational stress symptoms. Despite population differences, Khat ( a source of stimulant, cathine, and cathinone) chewing increased the odds of reporting psychological distress in an adult population in Ethiopia37 and another review of evidence38.

However, studies on the prevalence and factors of depression, and anxiety symptoms from a work-ascribed or causal perspective are not available in Ethiopia. Similarly, studies on the prevalence of occupational stress and its factors are limited in Ethiopia. Therefore, our study aimed to determine the prevalence of occupational stress, occupational depression, and job anxiety, and their associations with sociodemographics, health and behaviour, and work environment factors in healthcare workers.

Methods

Study setting, design, and period

We used a cross-sectional study design in nine selected hospitals located in six zones of the Central and Southern Ethiopia Regional States from 16th January to 28th February 2023. The healthcare facilities serve multiethnic, multicultural, and lingual populations located in six zones: Hadiya, Halaba, Kembata, Wolaita, Gurage, and Silitie zones. These zones are 132 to 328 away from the capital city of the country, Addis Ababa. The total population residing in the six zones is estimated to be more than approximately 9, 201,127.

The delivery of Ethiopian health care services involves three-tier systems: primary healthcare units, secondary healthcare units, and tertiary care. Primary care includes health posts, health centres, and primary hospitals. Health posts are healthcare facilities where health extension workers offer preventive universal health services at the Kebele level. Health centres additionally provide curative services. Primary hospitals offer inpatient and ambulatory care including emergency surgical services. Secondary healthcare units include general hospitals, receiving referrals from primary hospitals. Tertiary care hospitals include referral hospitals such as comprehensive specialized hospitals and teaching referral hospitals. Comprehensive specialized hospitals are referral hospitals that can provide advanced regional healthcare services and receive referrals from general hospitals and in some cases primary hospitals39.

During the data collection period, there were 317 public health centres and above public health facilities (health centres, primary, general, and referral(comprehensive specialised and teaching referral hospitals): 293 functional health centres, 16 primary public hospitals, 4 general hospitals, and 4 tertiary care hospitals (1 comprehensive specialised hospital, and 3 teaching and referral hospitals) during the previous southern Ethiopia administrative regions. Our study selected primary and those above based on two reasons:1 assuming that work-related psychosocial factors such as job demands, job demands, job control social support (resources) and workload are more prevalent and may increase the vulnerability of healthcare workers to common mental health symptoms;2 potential resource constraints to include health posts, and health centres due to large and dispersed geographic coverage and a large number of health facilities About 7,790 healthcare professionals were employed in the public health facilities in the six Zones from former central southern Ethiopia during the data collection period. Of these, nurses, midwives, public health officers, general practitioners, and medical laboratory professionals were 3905, 1467, 1138, 764, and 516, respectively.

Source and study population

The source populations were all healthcare professionals directly engaging in patient care and paramedic healthcare services in public primary hospitals, general hospitals, and tertiary hospitals. Healthcare workers who were included in the study from randomly selected healthcare facilities in each stratum composed of the study population.

Sample size determination

We calculated sample sizes for two PhD study objectives:1 to determine the prevalence of the three outcomes (occupational stress, occupational depression, and job anxiety) and their sociodemographic, health and behaviour, and work environment factors, and2 to assess the structural causal link between work-related psychosocial factors and the three mental health outcomes (symptoms of occupational stress, occupational depression, and job anxiety) using a structural equation modelling (SEM). Accordingly, we calculated a sample size for both study objectives and took the sample size calculated for the second objective because it provided a larger sample size. Before performing the logistic regression and analyzing the proportion of healthcare workers with occupational mental health symptoms, we planned to check the validity and composite reliability indices for our latent constructs (scales of occupational stress, occupational depression inventory, and job anxiety), which require a larger sample size. Therefore, we used the larger sample size estimated for the second PhD objective.

Following, the assumptions of structural equation modelling, a sample was calculated using a lower bound sample size for SEM developed by Westland JC40. Accordingly, in sample size calculation for the second PhD work, we used 86 observed variables and 10 latent variables with a statistical power of 80%, a medium anticipated effect size of 0.3, and a probability level (α) of 0.05 before our study. We found a minimum recommended sample size of 928 healthcare workers. We used a design effect of 1.5 to accommodate the error variation in the multistage sampling and a maximum of 10% of the nonresponse rate was considered from a nonresponse rate reported by 14 studies conducted among healthcare workers elsewhere in Ethiopia32. Finally, the minimum recommended a priori sample size for the study was 1531 healthcare workers and for the current study, the sample size was larger. Given that the sample size was estimated using a large sample assumption i.e. structural equation modelling, assures a more representative estimate of the target population and the conclusions drawn from our findings.

Variables and measures

Dependent variables

We measured occupational stress using a brief perceived occupational stress scale (POS-Scale)41. This tool assesses occupational stress in a cause-specific manner to determine whether psychosocial work stressors increase the likelihood of causing mental distress at work. The tool has been validated and has good psychometric properties for the public workforce including healthcare workers in the European context41(Cronbach’s alpha of 0.82). The scale has four items rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The item answers are averaged to provide ranges from 1(lowest perceived occupational stress) to 5(highest perceived occupational stress). Participants are asked to provide ratings that reflect their work stress in the last 6 months.

We used an occupational depression inventory24 to capture depressive symptoms in a work-ascribed manner. This tool has been validated to measure depressive symptoms from cause-specific perspectives or via etiological approaches adapted from clinical diagnostic criteria21. Participants rated the extent of the symptoms they had experienced in the past two weeks on a 4-point Likert scale from 0(never or rarely) to 3(nearly every day). Instead of assessing depressive symptoms in a “cause-neutral” manner, each ODI item assessed causal attributions to respondents’ work/job (e.g., “My experience at work made me feel like a failure”). The tool presented excellent reliability (Cronbach’s α of 0.916 )24 and showed strong reliability among South African employees including healthcare workers (Cronbach’s α was 0.926)42.

We used a short version of the Job Anxiety Scale (JAS-15)43 to measure work-ascribed general anxiety. The JAS-15 has five subscales each with three items: stimulus-related anxiety and avoidance behaviour (SAA), social anxiety and impairment cognition (SAIC), health and body-related anxiety (HBA), cognition of insufficiency (CI), and general job-related worrying (JRW). The tool has shown excellent internal consistency (Cronbach’s α was 0.942) in other studies43. After checking responses during the adaptation and pretest phases, we used a 5-point Likert scale ranging from 0 (no agreement at all) to 5 (total agreement) with no reverse-scored items. Like occupational depression, each JAS item indicated work factors as causal attributes to respondents’ jobs instead of assessing anxiety symptoms from a “cause-neutral” perspective (e.g. “After work, I hurry up more than others just to get away from that place.”).

Independent variables

We measured sociodemographics such as sex, age, marital status, educational level, monthly income, and other work environment characteristics such as work experience, violence at work, hours of work per week, and individual behavioural factors such as cigarette smoking, Chat or Khat (Catha Edulis) consumption, alcohol consumption, coffee consumption before sleeping or at night, and experience of planned physical exercise were also collected using standardized questionnaire adapted from previous literature.

Health and behavioural-related variables such as stressful life- threatening events (LTEs) in the previous 12 months, current general perceived health, history of disease and or injury in the previous 12 months, history of taking anti-depressants or painkillers in the previous 6 months, and sleep quality were collected. We assessed LTEs using an LTEs questionnaire containing 10 items with a “Yes” or “No” response, and validated elsewhere44 with a good reliability index (Cronbach’s alpha of 0.86). General perceived health was assessed with single-item questions adapted from validated tools elsewhere45, evaluating self-health by asking “How would you evaluate your health in general?”. Then, participants responded on a 5-point Likert scale ranging from 1 (poor) to 5 (excellent).

Sleep disorders (insomnia) were assessed using 3-item questionnaires adapted from the insomnia severity index (ISI)46 assessing sleep dissatisfaction, sleep interference with daily function, and sleep difficulty. This tool has good psychometric properties with reliability indices, and its Cronbach’s alpha ranges from 0.89 to 0.90. Accordingly, participants were asked to rate their extent of sleep in the previous 2 weeks from two items of sleep dissatisfaction and interference with daily function ranging from 1 (very satisfied) to 5 (very dissatisfied). One of the items for example was “Over the past two weeks, how satisfied are you with your current sleep pattern? Whereas, we assessed sleep difficulty by asking participants to rate their experience with at least one issue, such as difficulty falling asleep, staying asleep, or waking up early, on a scale from 0 (none) to 5 (very severe) .

Adaptation and pretesting procedures

Despite assuming that healthcare workers have an understanding of items of constructs when compared to the general population, the perceived and actual experience of common mental symptoms as a result of exposure to work-related psychosocial stressors may share a unique historical, social, and cultural phenomenon. Therefore, we planned to make sure that healthcare workers understand each item by clearly associating mental health symptoms with work-related issues and responding to them accordingly.

Accordingly, we adapted self-report measures for our setting following the cross-cultural adaptation steps of measuring instruments to reduce construct bias, method bias, and item bias47,48. First, two bilinguals (one MSc in community psychiatry and the other from nonclinical professions, MSc in hygiene and environmental health) translated the instruments from English into Amharic. Second, two translators and one of the authors (PhD candidate) discussed this through cross-checking with the original language of each item and reached a consensus. Third, one translated version was provided to an English university lecturer with an MA who was fluent in Amharic, and experienced in the study area. Another copy was given to another MA in English, who was also fluent in Amharic and well-versed in the study area, for back translation to English. Fourth, together with 4 translators, one of the authors and 3 invited experts (1 MSc in psychology, 1 MPH in epidemiology, and 1 MA in anthropology and protestant religion preachers of the areas) to check local cultural and language, experiential, and conceptual equivalence and produced a final questionnaire for pretesting. Finally, the questionnaire was pretested among 80 healthcare workers who were working in public health centres not included in the study by instructing them to document and provide feedback. After receiving the feedback, many points creating confusion were corrected. For example, the left-and right-hand responses of the 7-point Likert scale of the JAS were confusing for pretested participants. Hence, we changed it to a 5-point Likert scale and the participants were instructed to complete the scale from 0 (no agreement at all) to 5 (total agreement).

Inclusion and exclusion criteria

Healthcare workers who had worked for at least 6 months in clinical activities and paramedic activities at the current hospital or who were transferred from other hospitals were included in the study. We defined a minimum of six months because one of our outcome variables (occupational stress) requires at least 6 months of experience in any clinical and paramedical activities, and to reduce a recall bias. Those medical residents who were on speciality training for more than six months were included in the study. Those who were on annual leave or who changed professional activities for any reason during the data collection period were also excluded from the study. Healthcare workers who had been out of work for more than 2 weeks were also excluded from the study.

Sampling procedures

During the data collection period, there were 4 referral hospitals (1 comprehensive specialized teaching hospital, 3 teaching and referral hospitals), 4 general hospitals, and 16 primary hospitals. We selected 9 hospitals following the following procedures. First, we stratified hospitals into three groups: primary, general, and tertiary (referral hospitals include comprehensive specialised hospital, teaching and referral) hospitals. Within each stratum, we considered hospitals as clusters assuming that most of the working units and healthcare activities share nearly similar characteristics regarding work-related stressors. Second, we randomly selected 75%, of tertiary hospitals, 50% of general hospitals, and 25% of primary hospitals assuming that the complexity of work-related stressors, diverse units, and activities across specialities increase variability. Accordingly, 3 tertiary hospitals, 2 general hospitals, and 4 primary hospitals were randomly selected from each stratum. Third, the sample size was proportionally allocated to the size of each selected hospital. To ensure the representativeness of the sample, the sample size allocated for each hospital again was allocated to each working unit (the smallest unit of hospitals), which was stratified by professional category or speciality area obtained from the hospital’s directorate and human resource office. Whenever applicable, samples allocated for the work units were also allocated to speciality areas. Finally, all healthcare workers selected in each work unit were invited to participate until the allocated sample size was met. The schematic representation of the process of sampling procedure with proportionally allocating process and total samples before the data collection is displayed in Fig. 1.

Fig. 1
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Schematic representation of multistage sampling procedures among healthcare workers, central and southern Ethiopia, 2023.

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Data collection procedures

The data were collected using a self-administered structured questionnaire technique. We trained eleven data collectors about the purpose, content of the measurement tool, sampling procedures, how to support participants with inquiries about filling out the questionnaire, precautions during the distribution and returning it upon completion, and other ethical aspects. After the onsite training, each data collector took a minimum of 8 questionnaires to be pretested among healthcare workers who were working near each public health facility selected for the study. A week later, we collected feedback from each data collector. Finally, we duplicated and distributed a questionnaire for each study facility.

Following the sampling procedures and exclusion criteria, data collectors reached out to each health worker in each working unit of each selected hospital during working hours. Upon reaching out, participants were briefed about the purpose, benefits, and risks of the study, confidentiality, extent of autonomy, and justice for participating in the study. Each unit manager was individually informed of the purpose and the aim of the study. The data collectors also received written consent from each participant on the first page of the questionnaire. For those voluntary healthcare workers who were unable to fill out the questionnaire during the same day and time, the data collectors provided adequate time based on the consensus between the data collectors and the participants. We trained and assigned 3 collaborators for referral hospitals and 1 for primary hospitals from the facility’s unit heads or representatives to support the data collectors. In cases where healthcare workers took a questionnaire and lost it due to any inconvenience, the data collectors redelivered the reserve questionnaire to be completed. Finally, a completed questionnaire was returned to the data collectors to check its completeness.

Data processing, and analysis

Before the data entry, the questionnaires with incomplete responses on items were discarded. We used EPI-info version 7 to enter the data and exported it to SPSS version 25 for descriptive and ordinal logistic regression analysis. The SPSS file was exported to JAMOVI software for confirmatory factor analysis. The data were cleaned and checked for consistency and completeness. Initial data exploration was conducted for all variables ranging from socioeconomic and other work environment variables to each of our outcome variables. Outliers and items missing were checked. Outliers were controlled during the data entry by restricting the minimum and maximum values of the data, when necessary. Missing values of responses are substituted by means or medians depending on the nature of data distribution assuming that missing is completely at random (e.g. age). We performed univariate analysis to describe our study population. For sleep disorders, the sum of the mean scores of the three items was categorized into four from none to severe sleep disorders, and the sum score of LTEs was categorized into four for ease of data analysis.

We performed post-hoc measurement validity, reliability, and confirmatory factor analysis (CFA) to check whether items that measured outcomes of interest were in alignment with previously validated tools elsewhere24,41,42,43. The main purpose of performing CFA for each of our latent outcome variables was to check whether the measurement model fits the data, and to check the validity and reliability of the scales before determining the prevalence of mental health symptoms and their associated predictors. We performed the CFA using a structural equation modelling module of JAMOVI version 2.3.8 to allow us to use various robust estimation approaches and additional composite reliability and validity statistics. To handle data of skewed distribution with ordinal item responses, we used diagonal weighted least squares (DWLS) to estimate our parameters in the CFA. We fit POS and ODI as a unidimensional scale. Whereas, we fit the JAS with its five subscales to check the factor loadings of each subscale item. After checking each subscale item, we used the JAS as a unidimensional measurement of categorizing the score to determine the overall prevalence of job-related anxiety symptoms and its factors. Accordingly, all the CFA measurement models with three outcome variables had acceptable fit indices confirming the previous models for measuring the perceived occupational stress scale (POS), occupational depression inventory (ODI), and Job anxiety (JAS) fit for our data.

The reliability statistics also show that our measurements have good reliability scores. The ordinal alpha (α) (internal consistency based on polychromic correlations among items) was reported to account for unequal factor loadings and unequal error variances. The ordinal alpha (α) value for POS was 0.922 and for ODI was 0.958. The ordinal alpha (α) for subsequent JAS subscales ranged from 0.891 to 0.956. Omega hierarchy (Ѡ2) was used for internal consistency, accounting for item-specific weights for POS and ODI of 0.884 and 0.917. Regarding convergent validity, the average variance extracted (AVE) values for the indicators explained by POS was 0.758 and by ODI was 0.734. The average proportion of variations accounted for in a set of indicators by each subscale of JAS ranged from 0.746 to 0.878. For discriminant validity of the JAS subscales, the heterotrait-monotrait (HTMT) ratio of correlations between two pairs of subscales was less than 0.955. Specifically, the HTMT ratio was 0.954 for “job-related worrying and cognition of insufficiency”, and 0.929 for “health and body-related anxiety and cognition of in-sufficiency”., The values are larger than the recommended indicating one item was not well discriminated the concept than the other. However, the problem of this correlation could not be necessarily a problem for our estimation of the magnitude of overall job anxiety because the overall JAS has good reliability and convergent validity.

The univariate descriptive summaries, unstandardised, standardised factor loadings, the reliability and validity statistics, additional CFA information, and the covariances and correlation matrices of the measurement model for the three outcome variables are displayed in the supplementary Table S1-S4. The univariate descriptive summaries are displayed for the latent constructs of our outcome variables in supplementary Table S1. The standardised factor loadings, reliability and validity information are displayed in supplementary Table S2. Additional measurement model summaries are displayed in supplementary Table S3. Covariances and correlations matrices are also displayed in supplementary Table S4.

Following the confirmatory, reliability, and validity analyses, we made a dimensional severity cut-off of points for all the outcome variables. Accordingly, as the previously validated ODI-9 is based on the patient-health questionnaire, the scoring system is similar. Therefore, for occupational depressive symptoms, sum scores of 0 to 4, 5 to 9, 10 to 14, 15 to 19 and 20 to 27 were used to indicate “almost no symptom”, “minimal”, “mild”, “moderate” and “severe” symptoms, respectively. Because we could not find standard cut-off values for the job anxiety scale and occupational stress scale, we used percentile rank cases to show the severity of each disease. We followed the same procedure for job anxiety with similar labelling for job anxiety, 0, 16 to18, 19 to26, 27 to39, and 40 to 75 were categorized “no symptom”, “minimal”, “mild”, “moderate” and “severe” symptoms, respectively. For occupational stress, we used four classifications minimal (4 to 8), mild (9 to 10), moderate (11 to 12), and severe distress (13 to 16) because the “almost no stress” classification is not possible in stress theory.

Multiple variable ordinal logistic regression was done to assess independent sociodemographic, health, behavioural, and work environment factors with each common mental symptom (occupational stress, occupational depression, and job anxiety). We used multinomial probability distribution with a link function of cumulative logit from a generalized linear model because of failure to parallel lines test assumptions. Variables with a p-value of 0.25 or less for binary ordinal logistic regression were eligible for multiple ordinal logistic regression. Finally, model fit statistics (chi-square with degrees of freedom and p-value), tests of parallel lines, and an adjusted odds ratio of 95% are reported. The odds of moving from one category to the next were all checked for the explanatory variables (p-value < 0.05) based on model threshold values.

Results

Characteristics of the study participants

A total of 1426 healthcare workers from different professional categories completed the questionnaire, with a response rate of approximately 93.1%. The median age of the participants was 30 years, ranging from 22 to 58 years, and most participants were 27 to 30 years (60.5%). Most participants were males. Regarding educational status and profession, 79.7% held a degree, while 48.5% of the participants belonged to the nursing profession. In ethnic composition, 26.3% of participants were Hadiya, followed by 15.6% Wolaita. In terms of religion, 49.4% identified as protestant Christianity, followed by 28.8% Orthodox. Many of the study participants were married (48.5%) followed by single (47.8%). The majority reported a monthly estimated income between 115 and 213 USD, and family sizes ranged from 1 to 4 or more individuals. Table 1 presents the sociodemographic characteristics of study participants in the Central, and Southern Ethiopia Region.

Table 1 Sociodemographic characteristics of study participants in Central and Southern Ethiopia Region, 2023 (n = 1426).
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Table 1. Sociodemographic characteristics of study participants in Central and Southern Ethiopia Region, 2023 n = 1426)

Health, behaviour, and work environment characteristics of participants

Among study participants, 36.7% of individuals said their overall health was excellent followed by 31.1% who thought it was very good. A significant proportion of the respondents (33.7%) had a history of disease or injury in the previous 12 months of the data collection time. Approximately three of the ten healthcare workers reported using antidepressants or painkillers in the previous 6 months of the data collection time. Working while ill in the previous 6 months was reported by 32.4% of participants. Most healthcare workers reported that they never had engaged in any planned physical activity. More than half of the participants faced at least two out of ten LTEs in the previous 12 months. During the data collection, most individuals (98.0%) did not smoke cigarettes, did not chew khat (a stimulant herb known as ‘Catha Edulis Forsk’ in its scientific name ), and did not consume alcohol (84.9%). Approximately one in ten healthcare workers reported consuming coffee before sleeping or at night. According to the three-item measures of sleeping disorders, more than half of the participants had moderate to severe sleeping disorders. Table 2 presents a comprehensive overview of the health, behavioural, and work environment characteristics of the study participants.

Table 2 Health, behavioural and work environment characteristics of study participants in Central and Southern Ethiopia Region, 2023 (n = 1426).
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Table 2. Health, behavioural and work environment characteristics of study participants in Central and Southern Ethiopia Region, 2023 (n = 1426).

Prevalence of occupational stress, occupational depression, and job anxiety

Among 1426 healthcare workers, the two-week overall prevalence (score from minimal to severe) of occupational depressive symptoms was 39.0%. Among these, 9.3% of participants experienced mild occupational depressive symptoms, and 4.9% had moderate to severe symptoms. Of the 9 items, only 1.1% of participants scored 1 or above for item 9 (“I thought that I would rather be dead than continue in this job”), whereas the remaining participants scored none. Of those who reported at least one occupational depressive symptom in the previous two weeks, 407 (37.2%) of participants reported that they had an intention to leave or change jobs. Regarding job anxiety, nearly 6 in 10 healthcare workers scored from minimal to severe levels of job anxiety symptoms. More than 3 in 10 healthcare workers scored a mild or above level of job anxiety symptoms. Concerning occupational stress, more than 6 in 10 healthcare workers scored mild and above the level of six-month occupational stress. The prevalence distribution of symptoms of occupational mental health symptoms is displayed in Fig. 2.

Fig. 2
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Prevalence of common mental health symptoms in Central and Southern Ethiopia, February 2023.

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Sociodemographic and work-related determinants of common occupational mental symptoms

In the polytomous logistic regression for the ordinal responses of all three occupational mental health symptoms, parallel lines test assumptions were uncertain, and the log-likelihood values in all cases were zero and maximum likelihood estimates did not exist. Therefore, ordinal logistic regression with multinomial probability distribution with a link function of cumulative logit from generalized linear models was used for all three outcome variables to identify factors for each outcome variable.

Our data fits the multiple variable ordinal logistic regression model well for occupational depressive symptoms, supported by an acceptable Pearson chi-square test (X2(6237.21) = 5453, X2/df = 1.144) and a significant Omnibus test (X2(428.149) = 31, p < 0.001). The threshold values show the explanatory variables significantly influenced the odds of moving between categories (p < 0.05). Accordingly, female healthcare workers were 1.319 times more likely to experience occupational depressive symptoms than male healthcare workers (AOR:1.319, 95%CI:1.027,1.695). Compared with medical specialists, participants with bachelor’s degrees were about 44%, and those with master’s degrees were 27% less likely to report occupational depressive symptoms. Study participants who did not report a previous history of disease or injury in the previous four weeks were approximately 30% less likely to report occupational depressive symptoms compared to their counterparts (AOR: 0.702,0.95% CI:0.517,0.953). Healthcare workers who were very dissatisfied with their job (AOR: 2.197, 95%CI: 1.388, 3.476) and those who were dissatisfied (AOR: 2.197, 95%CI: 1.486,3.247) were approximately twice as likely to report occupational depressive symptoms compared to those who were very satisfied healthcare workers. A one-unit increase in the mean score of sleeping disorder, a score of LTEs, and working hours per week significantly increased the odds of reporting occupational depressive symptoms by 1.82, 1.16, and 1.01, respectively.

For job anxiety symptoms, the Pearson chi-square (X2 = 5908.88, df = 5361, X2/df = 1.102) and the Omnibus test (likelihood ratio chi-square X2 = 510.64, df = 23, p < 0.001) indicate a good model fit. The odds of moving between categories were significantly influenced by the explanatory variables (p < 0.05), based on the model’s threshold values. According to the multivariable ordinary logistic regression, healthcare workers who judged their general health condition as “excellent” (AOR: 0.316, 95CI: 0.127,0.786), were approximately 32% less likely, and those with “very good”(AOR:0.364, 95CI:0.146,0.903) were about 36% less likely to report job anxiety symptoms compared to those who judged their health as “poor”. Compared to very satisfied healthcare workers “very dissatisfied” (AOR:1.98, 95%CI:1.323, 2.976), and “dissatisfied” (AOR: 2.420, 95%CI: 1.731,3.383) were approximately two fold twice as likely to report job anxiety symptoms. Participants with no history of verbal violence at work were less likely to report job anxiety symptoms compared to their counterparts. As a unit of increase in scores of sleeping disorders, and a score of life-threatening events (LTEs) significantly increased the odds of reporting job anxiety, and occupational stress symptoms by 1.63 and 1.17.

For occupational stress, the Pearson chi-square (X2 (4360.75) = 4186.00, X2/df = 1.041) and the Omnibus test (X2(246.61) = 26, p < 0.001) indicated a good model fit. However, no significant threshold values were found suggesting that the odds of moving from one category to the next remain relatively constant across adjacent categories (p-value > 0.05). Accordingly, healthcare workers who had a diploma (AOR: 0.285, 95%CI: 0.120, 0.677), bachelor’s degree (AOR: 0.282, 95%CI: 0.124, 0.642), or master’s degree (MPH or MSc) (AOR: 0.250, 95%CI: 0.101, 0.630) were nearly a quarter less likely to report occupational stress symptoms than medical specialists. Compared to those who chewed Khat, those who did not chew Khat were less likely to report occupational stress symptoms (AOR: 0.633, 95%CI: 0.453, 0.884). Healthcare workers who did not consume the coffee immediately before sleeping or at night were less likely to report occupational stress symptoms than those who consumed coffee (AOR: 0.791, 95%CI: 0.638, 0.981). Compared to those healthcare workers who were very satisfied with their jobs, those who judged their satisfaction as “very dissatisfied” (AOR: 1.923, 95%CI: 1.305, 2.834) or “dissatisfied” (AOR: 2.075, 95%CI:1. 506, 2.860) were about twofold more likely to report occupational stress symptoms. Healthcare workers who did not experience verbal violence were less likely to report occupational stress symptoms (AOR: 0.759, 95%CI: 0.601, 0.959). Being a young healthcare worker significantly increased the odds of reporting occupational stress symptoms by 0.978.

Table 3 Multiple ordinary logistic regression results of predictors of occupational mental symptoms among health workers in Central and southern Ethiopia, 2023.
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Table 3. Multiple ordinary logistic regression results of predictors of common occupational mental symptoms in healthcare workers, in Central and southern region Ethiopia, 2023.

Discussion

According to our new work-ascribed or cause-specific measures, the study found a high prevalence of occupational depressive symptoms, job anxiety, and occupational stress among Ethiopian healthcare workers. The likelihood of reporting a higher prevalence of these mental health symptoms was significantly associated with various sociodemographic, health behavioural, and work-related factors. Accordingly, being female, being at the educational level of medical specialists, individuals with a history of illness or injury, those experiencing low job dissatisfaction, higher scores for sleep disorders and life-threatening events (LTEs), and longer working hours were more likely to report occupational depressive symptoms. Similarly, participants with low job satisfaction, a history of workplace verbal violence, higher scores for sleep disorders, and high LTE scores had significantly greater odds of reporting both job anxiety and occupational stress symptoms. Participants with poor perceived health were significantly more likely to report job anxiety symptoms while being young significantly increased the odds of reporting occupational stress symptoms.

Addressing mental disorders requires considering complex psychosocial causal pathways including work-related stressors aligning with the original biopsychosocial health model7 and recent explanatory suggestions8 for mental disorders. This study provides valuable evidence for the current scientific understanding, targeted prevention and control of common mental health problems in working populations. Therefore, determining the magnitude of common mental symptoms through cause-specific measurements provides important clues for quantifying the severity of the problem and individual ways of making sense of the symptoms. Similarly, assessing determinants such as sociodemographic, health and related behaviours, and work-environment variables could pave the way for understanding causation. It can provide clues for designing targeted control and prevention policies and professional practices. In line with this, the present study determined the prevalence of depressive symptoms, anxiety, and distress in a “cause-specific” or “etiological sense” and assessed the determinants of each occupational mental symptom. Despite many debates, we believe that our study highlighted the magnitude, and factors of these mental disorders, and one can use terminologies such as “occupational depression”, “job-related anxiety”, and “occupational stress”, and reflect and measure the problems of these mental disorders in the working population.

While work-related stressors were key in selecting and stratifying the nine hospitals, some individual and job characteristics may be overrepresented in tertiary hospitals and underrepresented in primary hospitals. To account for this selection bias, we tried to form strata followed by a random selection of facilities from each stratum in our multistage sampling processes. The use of a larger sample size which could not have been calculated using conventional methods, performing an intensive cultural adaptation of measuring tools, and checking measurement validity, and reliability before could also provide us with conclusive evidence. We believe the hospital selection’s disproportionate representation does not impact generalizability, as key factors like resources for managing work stress are consistently limited across hospitals due to the lack of well-established workplace well-being in the country. Therefore, the findings of our study would still apply to the healthcare workers working in primary, secondary and tertiary care services with some shortcomings.

We could not find more studies that used ODI24 to measure depressive symptoms. Therefore, we compared our findings with those from studies using cause-neutral approaches like the PHQ-921 neutral approaches such as using measures. Accordingly, the prevalence of moderate to severe occupational depressive symptoms in our study (4.9%) was nearly four times less than that of healthcare workers working in tertiary hospitals in Addis Ababa16. Two main possible explanations can justify this difference. First, the ODI has been validated to capture the unique context of work-related depression or to capture depressive symptoms only due to work-related causes. In contrast, the PHQ-9 captures broader forms of distress without considering specific causes. As a result, the prevalence of depressive symptoms measured by the ODI could be less than the prevalence measured by the PHQ-9. Second, the study findings from Addis Ababa during the COVID-19 pandemic could add fuel to existing work-related causes of distress as indicated by a global study49. Nevertheless, the overall prevalence of depressive symptoms is greater than the overall prevalence (above minimal symptoms) among adults in the general population50. In addition, despite the good psychometric properties of ODI reported elsewhere24,42,51 and confirmed before its prevalence was determined, item 9 (suicidal ideation due to work-related factors) may slightly reduce the overall prevalence of all forms of occupational depressive symptoms. Our findings may motivate researchers to conduct stronger designs of the study such as cohort studies to establish causality. Despite the difficulty of declaring causality due to the cross-sectional nature of our study, the findings may still inform a greater relative contribution of work-related factors to a greater prevalence of depressive symptoms. Therefore, our findings call for the prioritization of establishing workplace mental health policies and prevention strategies for healthcare workers to better attain well-being, productivity, and quality healthcare services.

Similarly, we could not find studies that specifically determined the prevalence of job anxiety using measures for comparison purposes in the Ethiopian context, and the moderate to severe prevalence of job anxiety symptoms was less than that in other contexts in Ethiopia13,14,15,16. A similar analogy of reasons with the depression case we discussed before could be cited16. However, the overall prevalence (in terms of minimal to severe forms) in our study was also much greater than that in the general population as evidenced by studies on general anxiety49 and even the studies during the pandemic period13,16. Despite confirming a very good psychometric property for the JASbefore determining the prevalence, the correlations between two pairs of subscales (“subscales of job-related worrying and cognition of insufficiency”, and “health and body-related anxiety and cognition of insufficiency”) were greater than 0.90 indicating some issues in discriminant validity. However, we believe that the problem may not make a difference in the underestimation of the prevalence of job anxiety. The prevalence of general job anxiety may differ across sub-dimensions (stimulus-related anxiety and avoidance behaviour (SAA), social anxiety and impairment cognition (SAIC), health and body-related anxiety (HBA), cognition of insufficiency (CI), and general job-related worrying (JRW)43,52. However, did not perform analysis per each subdimension due to difficulties in finding the cut-off points for each subscale. Since the job anxiety scale has no time bounding, the prevalence of job anxiety is much higher, as is the prevalence of depressive symptoms.

Regarding the prevalence of occupational stress, a comparison may not have a meaningful interpretation due to the diverse measures used by previous studies20. Keeping in mind these differences, the moderate to severe level of occupational stress in our findings is less than that reported by a review of healthcare workers in Ethiopia20. However, because we believe that our study captures only the distress related to work stressors, the magnitude of calls implies the need for workplace stress management interventions and welfare packages for healthcare workers.

We believe in the availability of information on the determinants of depression, anxiety, and psychological distress measured in nonwork-ascribed approaches for the general population. However, additional evidence is required to better understand the social determinants of mental health for the working population including healthcare workers to attain sustainable goals2. Accordingly, our ordinal logistic regression revealed that being female increased the risk of experiencing occupational depressive symptoms. This finding is in line with the findings of studies from Ethiopia13, and another global study of the general population49 despite differences in measurement approaches and contexts. According to our findings, those who have a medical speciality education significantly experienced occupational depressive symptoms. Our finding is also consistent with findings from northern Ethiopia14. This could mainly be due to previously documented reasons such as a higher workload due to workforce shortages or low decision latitude53, frequent exposure to severe clinical incidents like medical errors54,55, shortage of relaxing and socialization time, and higher effort to reward ratio56 compared to bachelor’s degree or master’s degrees. Because of the underrepresenting nature of medical specialists in the Ethiopian health workforce system, there might be bias that could potentially affect the generalizability of our findings. Therefore, we do not believe that our findings necessarily or exclusively imply a special workplace well-being package segregated by educational level but rather provide insight to further exploration of solutions. Future research should aim to recruit a more demographically balanced sample to validate these findings and ensure they are applicable across the entire target population.

A history of disease or injury in the previous four weeks increased the risk of experiencing occupational depressive symptoms compared to their counterparts. This finding is consistent with findings from Ethiopia14 despite the use of measurement approaches. General job dissatisfaction was the strongest predictor of occupational depressive symptoms despite the difficulty in finding studies specifically for healthcare workers. However, high job dissatisfaction among Ethiopian healthcare workers25 could indirectly support our findings despite no studies previously established the association with depressive symptoms in our context. Therefore, healthcare workers’ sources of dissatisfaction with their jobs should be further mitigated, and sustainable interventions should be implemented to solve this problem. Higher scores for the severity of sleeping disorders, LTEs and working hours per week significantly increased the risk of occupational depressive symptoms. Despite the unavailability of studies on these factors among Ethiopian workers, other studies support that sleep disorders such as insomnia26,27,28,29 develop depressive symptoms. Similarly, studies showing the association between life-threatening events and depressive symptoms are not available for comparison in Ethiopian healthcare workers. However, other studies in other populations have shown that LTEs increase the risk of depression30,31. Despite the lack of studies in Ethiopian contexts, our findings are supported by studies that show more working hours per week increase the likelihood of depressive symptoms29,32.

According to our multiple ordinary logistic regression, poor general perceived health, dissatisfaction with one’s job, a history of verbal violence at work, a high score for sleeping disorders, and a score for LTEs significantly increased the odds of reporting job anxiety symptoms. We could not find studies typically conducted on healthcare workers who measured work-related anxiety from Ethiopian healthcare workers but limited to the context of high countries. However, studies that measured generalized anxiety from other contexts using non-cause specific measures supported our findings that poor perceived health, job dissatisfaction among nurses33,34, forms of harassment such as a history of verbal violence6, higher sleep disorder scores35, and higher (LTEs)31 scores increase the likelihood of anxiety symptoms. However, further studies with stronger designs such as cohort studies are required.

Similar to our findings from our multiple ordinary logistic regression analyses, studies have also shown that being from a medical speciality14, low job satisfaction, verbal violence, higher (LTEs) scores, higher sleep disorders scores, and being at a younger age increase the likelihood of occupational stress symptoms. But, we would like to remind readers that we had measurement differences from what other studies used to measure. Despite limited studies showing the association between consuming coffee immediately before sleeping or at night and occupational stress for our target population, the association is indirectly supported by the findings that consuming coffee (a source of caffeine)36 at bedtime (3–6 h bedtime) results in significant sleep disturbance and affects daytime functioning57, which may increase occupational stress. Despite population and measurement differences, the associations between Khat chewing (a source of stimulant, cathine, and cathinone) and stress in an adult population in Ethiopia37 and another review of evidence38 support our finding. Khat chewing may also demand additional life costs due to the reasons that the expenditure for the khat could become a competing event preventing from fulfilling other survival needs leading to financial strain, a deficit in social capital, low performance when facing a shortage of Khat, poor communication with colleagues and supervisors etc.

However, as displayed in the multiple ordinary logistic regression table (See Table 3), other sociodemographic factors (e.g., sex with anxiety and occupational stress, and age with depression and anxiety), health and related factors ( e.g., general perceived health with occupational depression and occupational stress, physical exercise and smoking with the three outcomes), and work-environment factors (e.g., physical violence with all the three mental health outcomes) were not significantly associated factors in our study. Despite many reasons that could be mentioned, the most common reason could be recalling bias, and the cross-sectional nature of the study. This could be due to reasons such as recall bias.

Strengths and limitations of the study

To our knowledge, the study is the first to determine the prevalence of common mental health symptoms in a cause-specific or work-ascribed manner. The magnitude of the proportion of health workers affected by the three mental health symptoms informs a clear message about the contribution of work stressors to the problems, which have not yet been addressed by previous studies, and could contribute to targeted interventions to improve the mental well-being of healthcare workers. As a result, the paradigm for measuring common mental symptoms could be scaled to other occupational contexts to investigate occupational mental health issues. Despite not the main purpose of this study, the CFA results, reliability and validity checks brought good news offering occupational mental health researchers a useful tool to monitor common mental health symptom severity at the institutional level. In the process of our reliability, and validity checks of measures.

The sample size used for the study is larger than twofold than what could have been calculated using conventional methods such as the single population proportion formula. In addition to providing a more representative sample of the target population, this sample also enabled us to perform confirmatory factor analysis to check the validity and reliability of our measures before directly getting to estimate the prevalence, and determinants of those common mental health symptoms. In our multistage sampling process, we recruited participants through stratifying working units, and specialities aiming to achieve a sample representative of the target population, healthcare workers with each category. Furthermore, performing an intensive cultural adaptation of measuring tools, providing extra and convenient time when healthcare workers are unable to complete questionnaires on the same day, assigning collaborating and supporting unit heads for the data collector through adequate training and providing a reserve questionnaire when healthcare workers lose the former questionnaire, in any case, could also minimize biases and yielded high response rate.

However, while our sample closely matched the target population in most respects, there were overrepresentations of nurses and underrepresentation of medical specialities due to the inherent nature of variabilities of the health workforce in the country in general, the study area in particular. These variations might introduce bias, potentially affecting the generalizability of our findings. However, the cross-sectional nature of our study could limit the causal relationship between predictors and the outcome variables. We also faced difficulties in comparing findings about the association between the factors because of a lack of studies that measured depressive and general anxiety symptoms in a work-ascribed manner. However, we are uncertain in our comparison because of the lack of studies in Ethiopia and limited studies around the globe that measure depression and general anxiety from work-related perspectives. Hence, we made comparisons with the findings of the studies that measured depressive symptoms and general anxiety symptoms using non-cause-specific tools derived from the DSM-5. However, capturing important factors previously identified by other studies conducted using a cause-neutral manner implies that our measurement had also criterion validity.

Conclusion

In our new perspective measurement of common mental symptoms, we found a higher prevalence of occupational depressive symptoms, job anxiety, and occupational stress. This implies a higher relative contribution of work-related causes among healthcare workers.

Our findings highlighted that being female, having a medical speciality of education level, history of disease or injury in the previous four weeks, having job dissatisfaction, having higher scores of sleeping disorder, a higher (LTEs), and having higher working hours per week significantly increased the odds of reporting occupational depressive symptoms. Similarly, participants with low job satisfaction, a history of workplace verbal violence, higher scores for sleep disorders, and higher LTEs scores had significantly greater odds of reporting both job anxiety and occupational stress symptoms. Participants with poor general perceived health were more likely to report job anxiety symptoms. The odds of reporting occupational stress symptoms were significantly higher for those with a medical speciality education and for younger participants.

Despite lacking studies for more comparisons of our findings with a similar measuring paradigm, our findings suggest the severity of the magnitude and call for the prioritization of establishing workplace mental health policies and prevention strategies for healthcare workers to better attain well-being, productivity, and quality healthcare services. The findings highlight the importance of addressing gender-based interventions, educational level, job satisfaction, sleep hygiene, managing LTE, violence prevention, and management of working hours per week to enhance the well-being, productivity, and quality of healthcare services of healthcare workers in Ethiopia.