Journal of Depression and Anxiety
Open Access
ISSN: 2167-1044
+44 1223 790975
ISSN: 2167-1044
+44 1223 790975
Research Article - (2015) Volume 4, Issue 3
Background: Health care is a demanding profession physically and mentally which contributes risk for mental distress. Even though it studied well in economically advanced nations, little is known among health professionals in Africa. The aim of this study was to assess prevalence and risk factors of mental distress among health professionals.
Methods: This study was conducted from November 15, 2013 to December 15, 2013 at JUTH using an institution based cross-sectional study design. All health professionals (N=403) were included from various disciplines. Selfreport questionnaire (SRQ-20) was used to detect mental distress. Other structured questionnaire related to sociodemographic characteristics, burnout, work related condition, and substance use habits were used to collect data.
Result: Out of the total study participants (n=334), 29.9% (n=100) of them were found to have mental distress. Prevalence of mental distress among substance user health professionals was higher compared with non-users. After adjusting for potential confounders, mental distress was 4.47 times higher among participants with high burnout score (AOR=4.47, 95%CI=2.37-8.44). Additionally, the likelihood of developing mental distress among physically and verbally abused staffs was 2.34 times higher than their counterparts. Also it was more than two times higher among health professionals reported poor prospect of promotion than those who reported good prospect of promotion.
Conclusion: Identified association of work related factors with mental distress needs for immediate and far-reaching interventions of stress reduction therapy. Finding adequate prevention strategies to combat burnout seems therefore very crucial.
Keywords: Burnout; Ethiopia; Health professionals; Mental distress; Work related factors
CBIS: Copenhagen’s Burnout Inventory Scale, ETB: Ethiopian Birr, JUTH: Jimma University Teaching Hospital, SARS: Severe Acute Respiratory Syndrome, SPSS: Statistical Package for Social Sciences, SRQ: Self Reporting Questionnaire.
Mental distress is combination of abnormal thoughts, emotions and behavior which significantly affects normal life style of individuals in areas of self-efficacy, self-autonomy, competence and the ability to realize one’s own intellectual and emotional potential [1,2].
Different studies proved that the incidence and prevalence of mental disorders in the working population is getting increased in the last decades. Mental distress affects 15 up to 25 percent of working population globally [3,4].
Mental distress is contributing greater impact negatively on global economy by affecting human resource through increasing absenteeism from work, sick leave, compensations and by other different reasons [5,6]. Mental distress is the most common problem among health professionals working in health care facilities due to psychosocial challenges, high work related stressors and other socio-economic factors [6,7]. Because, Health profession is always demanding physically and mentally which requires careful and clever decisions in life and death issues in short span of time, with limited resources at hand especially at the time of medical emergency [8].
Work and work organization conditions affect psychological wellbeing of employees as a result of excessive working hours, time constraints for their families, conflict with staffs, role ambiguity and job insecurity. Poor chances for advancement (promotion), verbal or physical harassment from superiors, health and safety risks were also identified sources of mental distress at work place [5,9,10].
Working environment and co-worker communication, administrative issues (office politics and competition), family relationship and level of income were found to be highly associated with the occurrence of mental distress among health professionals; especially when employees fail to purchase adequate food, clothing and services in their salary which affects their self-esteem as well as the sense of control over one’s life [3,11].
Mental distress was found to be a big treat for institutions to deliver the required goals due to increasing in the turnover rate of staffs, shifting profession to non-clinical ones [10]. Many of lost work days each year are due to absenteeism, sick leave and unreasonable leave from work are attributed to mental distress and most of health professional employees consider quitting their jobs because of complaining about emotional instability and unable to solve stressful conditions of their work [12-14].
Health professionals with mental distress could not deliver the required care and unable to play their crucial role to their health care facility and to their community especially in the time of advanced health care technologies, complex care processes, complex patient needs, and complex organizational systems [10,14].
Mental distress due to stressful working conditions is the second most compensated case to employees following musculoskeletal disorders in Australia [15]. A study done in Nepal among a tertiary care staffs working at different departments found that, 34.7% prevalence of mental distress among nursing staffs [16]. Another study conducted in University of Limpopo, South Africa on 109 nurses’ found that, the majority of respondents were unhappy with Payment; 79% of them said they were not paid enough for what they did and for what they deserved; and 60% of them felt that the organization should increase their payment considering to their demanding work [17].
This study aimed to assess prevalence of mental distress and associated risk factors among health professionals working in tertiary teaching hospital and to generated baseline information.
An institution based cross-sectional study was conducted in Jimma University teaching hospital (JUTH) found in Jimma town. Jimma town is located 356 km southwest of Addis Ababa, the capital city of Ethiopia. JUTH is a teaching and tertiary level hospital and provides (or caters) inpatient and outpatient health service for around 15 million people living in southwest of Ethiopia. JUTH has a total of 11 wards (inpatient department), 523 beds and 403 permanently employed health professional staff.
This study was conducted in 2013 by using a cross-sectional study design. The aim of this study was to assess prevalence and risk factors of mental distress among health professionals working in JUTH. Self administer questionnaire was distributed to all health professionals working in JUTH (N=403) from November 15, 2013 to December 15, 2013 by recruited data collectors. Health professionals who were on grief in the last two month of data collection period and who were providing a free service for the hospital were excluded.
Mental distress was dependent variable used in multivariate analysis. It was screened by using the SRQ-20 items with cut-off point 6 and above scores. Scores below six points recoded for “No Mental Distress” and scores 6 and above recoded as “Yes for Mental Distress” again and analyzed through bivariate and multivariate logistic regression with independent variables.
The self-reporting questionnaire (SRQ-20) tool was used to detect mental distress. This instrument was developed by the WHO to screen mental distress in primary health care settings and community of lowincome countries [18]. It asks about features of mental distress with “YES” or “NO” responses in each question over the past 1 month [18,19]. SRQ has been used widely validated for epidemiological studies in clinical and community settings in Ethiopia and in Africa [19,20]. The Sensitivity and specificity of this instrument in community setting was 0.85 and 0.94 respectively [21]. Mental distress was detected at a cut-off point 6 and above “YES” scores. Burnout was assessed by using Copenhagen’s burnout inventory tool which was used and validated in different studies [22,23]. It asks about emotional and physical disturbances in terms of severity and frequency and found valid tool to assess level of burnout among professionals working in health care services. Alcohol use disorder was assessed by using CAGE tool at score of 2 above “YES” answers among four questions [24].
It is a valid and reliable screening tool for detection of alcohol use problems and used in different studies in Ethiopia. It consists of four simple questions asking multidimensional areas about alcohol use. Other structured questions were used to assess socio-demographic characteristics, health related conditions and substance use.
Health profession
In this study refers to all trained and certified professionals who had direct involvement in health care service. It included physicians, nurses, pharmacist/druggist, laboratory technicians/ technologists, physiotherapist, anesthetist, sanitarian, x-ray technician and others.
Data were coded, entered, cleaned and analyzed by using SPSS version 16. Dependent and independent variables were entered in to bivariate logistic regression one by one to detect association of independent variables with outcome variable. Age, salary and service year were entered to bivariate logistic regression considered as continuous variables to identify their association with mental distress. All variables associated with mental distress at bivariate logistic regression were entered to multivariate logistic regression once to control potential confounders. Variables with p-value less than 0.05 in multivariate regression were declared to be independent predictors of mental distress.
Ethical clearance was obtained from Jimma University, College of Public Health and Medical Sciences ethical committee. Detailed information about the objective of the study was explained to all participants before questionnaire administered. Informed consent was obtained from each participant before starting data collection and confidentiality of each participant was kept.
Characteristics of participants
Among 403 permanent employee health professionals of JUTH; 83% (n=334) of them were participated in this study. Majority of the participants were male (64.7%, n=213) and the mean age (standard deviation) of participants was 28.6 ± 7.65 years. The mean monthly salary of the study participants was 2119.9ETB with standard deviation of 772.09 and mean of service year in the same institution was 4.57 with standard deviation 6.61.
Half of the total participants were single in marital status (50.2%) and 3.3% (n=11) were divorced. Three-fourth of the participants were nurses. Out of the total participants majority (52.0%, n=172) of them were first degree holders (Table 1). Even though monthly salary analyzed as continuous variable to identify its association with mental distress, it was grouped by quartile to identify prevalence distribution of mental distress among age groups by using cross-tabulation.
| Socio-demographic variables | Number | % | |
|---|---|---|---|
| Sex | Male | 213 | 64.7 |
| Female | 116 | 35.3 | |
| Religion | Orthodox | 156 | 46.7 |
| Protestant | 108 | 32.3 | |
| Islam | 63 | 18.9 | |
| Others1 | 7 | 2.1 | |
| Ethnicity | Oromo | 166 | 49.7 |
| Amhara | 92 | 27.5 | |
| Tigre/wolayta/Guragie | 33 | 9.9 | |
| Dawuro/keficho | 24 | 7.2 | |
| Others2 | 43 | 5.7 | |
| Marital status | Single | 167 | 50.2 |
| Married | 114 | 34.2 | |
| In relationship | 39 | 11.7 | |
| Divorced/widowed | 13 | 3.9 | |
| Monthly salary (ETB) | ≤1434 | 85 | 25.4 |
| 1435-2190 | 83 | 24.9 | |
| 2191-2602 | 102 | 30.5 | |
| ≥2603 | 64 | 19.2 | |
| Profession | Nurse | 237 | 71.8 |
| Pharmacist | 29 | 8.8 | |
| Lab. technologist | 25 | 7.6 | |
| Physician | 15 | 4.5 | |
| Others3 | 24 | 7.3 | |
| Academic status | Degree | 172 | 52.0 |
| Diploma | 138 | 41.7 | |
| General practitioner | 11 | 3.3 | |
| Others4 | 10 | 3.0 | |
| Have children | No | 223 | 66.8 |
| Yes | 111 | 33.2 | |
Note: 1Catholic, Jehovah and no religion; 2Yem, Harari, Siltie; 3Psychiatrist nurse, Physiotherapist, anesthetist nurse and sanitary environmentalist; 4Specialist and master degree.
Table 1: Socio-demographic characteristics of health professionals working in JUTH, December, 2013 (n=334).
Prevalence of mental distress
Prevalence of mental distress among health professionals working in JUTH was 29.9% (n=100). Of the total participants 31.8% (n=35) of women and 29.4% (n=62) of male were found to have mental distress respectively. Out of the total study participants 34.8% (n=31) of Amhara and 26.5% (n=43) of Oromo ethnic groups were found to have mental distress (Table 2).
| Variables | Mental distress | COR(95%CI) | p-value | ||
|---|---|---|---|---|---|
| Yes | No | ||||
| No (%) | No (%) | ||||
| Working dep’t | OPD IPD OR ICU Laboratory Pharmacy Others |
12(23.1) 54(37.0) 5(25.0) 6(33.3) 6(27.3) 2(14.3) 14(26.9) |
40(76.9) 92(63.0) 15(75.0) 12(66.7) 16(72.7) 12(85.7) 38(73.1) |
1.80(0.35, 9.19) 3.52(0.76, 16.33) 2.00(0.33, 12.18) 3.00(0.50, 17.95) 2.25(0.39, 13.17) Ref. 2.21(0.44, 11.14) |
0.480 0.108 0.452 0.229 0.368 0.336 |
| Perception of one’s professional role | Yes No |
83(28.7) 15(45.5) |
206(71.3) 18(54.5) |
Ref. 2.07(1.00, 4.30) |
0.051 |
| Pare time work/ Attending school | Yes No |
23(25.6) 76(32.5) |
67(74.4) 158(67.5) |
Ref. 1.40(0.81, 2.42) |
0.227 |
| Night shift work | Yes No |
51(34.2) 48(27.4) |
98(65.8) 127(72.6) |
1.38(0.86, 2.21) Ref. |
0.188 |
| Interest of one’s own profession | Yes No |
36(21.3) 63(40.6) |
133(78.7) 92(59.4) |
Ref. 2.53(1.55, 4.12)* |
0.001 |
| Medical faults status | Yes No |
25(47.2) 74(27.4) |
28(52.8) 196(72.6) |
2.37(1.30, 4.32)* Ref. |
0.005 |
| Physical/verbal abuse | Yes No |
61(42.7) 38(21.1) |
82(57.3) 142(78.9) |
2.78(1.71, 4.53)* Ref. |
0.001 |
| Support regarding work | High Low |
90(29.1) 6(40.0) |
219(70.9) 9(60) |
Ref. 3.65(1.27, 10.55)* |
0.017 |
| Relationship at work place | Good Not good |
48(24.4) 50(40.0) |
149(75.6) 75(60.0) |
Ref. 2.07(1.28, 3.36)* |
0.003 |
| Perception of management system | Good Not good |
29(21.5) 69(36.9) |
106(78.5) 118(63.1) |
Ref. 2.14(1.29, 3.55)* |
0.003 |
| Level of burnout | High Low |
58(51.8) 41(19.6) |
54(48.2) 168(80.4) |
4.40(2.66,7.29)* Ref. |
<0.001 |
| Prospect of Promotion | Good Poor |
33(21.4) 63(37.7) |
121(78.6) 104(62.3) |
Ref. 2.22(1.35, 3.65)* |
0.002 |
| Perception of work load | Yes No |
90(32.1) 9(20.5) |
190(67.9) 35(79.5) |
0.54(0.25, 1.18) Ref. |
0.122 |
| Perception of working environment | Suitable Not suitable |
21(18.3) 76(36.7) |
94(81.7) 131(63.3) |
Ref. 2.60(1.50, 4.51)* |
0.001 |
| Perception of professional Recognition |
Yes No |
36(23.7) 63(36.6) |
116(76.3) 109(63.4) |
Ref. 1.86(1.15, 3.03)* |
0.012 |
| Appropriate salary | Yes No |
12(22.2) 87(32.3) |
42(77.8) 183(67.8) |
Ref. 1.66(0.83, 3.32) |
0.148 |
| Resource availability | Yes No |
73(36.5) 27(21.6) |
127(63.5) 98(78.4) |
2.09(1.25, 3.49)* Ref. |
0.005 |
| Fear of contracting an illness during work | Yes No |
66(38.2) 33(21.9) |
107(61.8) 118(78.1) |
2.21(1.35, 3.61)* Ref. |
0.002 |
| Job insecurity | Yes No |
35(39.8) 65(27.5) |
53(60.2) 171(72.5) |
1.74(1.04, 2.90)* Ref. |
0.035 |
Table 2: Bivariate logistic regression: Association of work related factors with mental distress among health professionals working in JUTH, December, 2013(n=334)
Out of the total participants, 47.4% (n=91) were identified to have high score of burnout (above the mean score). Prevalence of mental distress among health professionals who found to have high burnout score was 51.8% (n=58). The prevalence of mental distress among health professionals working at inpatients department was 37.0% (n=54). Prevalence of mental distress among participants who were working at night shift most of the time in the last one month was 34.2% (n=51) (Table 3).
| Variables | Mental distress | COR(95%CI) | p-value | ||
| Yes | No | ||||
| No (%) | No (%) | ||||
| Sex | Male Female |
62(29.4) 35(31.8) |
149(70.6) 75(68.2) |
Ref. 1.12(0.68, 1.85) |
0.652 |
| Religion | Orthodox Protestant Other1 |
59(39.1) 21(20.0) 20(29.0) |
92(60.9) 84(80.0) 49(71.0) |
2.57(1.44, 4.58)* Ref. 1.63(0.81, 3.31) |
0.001 0.174 |
| Ethnicity | Oromo Amhara Tigre/Wolayta/Gurage Other2 |
43(26.5) 31(34.8) 11(55.0) 15(27.8) |
119(73.5) 58(65.2) 9(45.0) 39(72.2) |
Ref. 1.48(0.85, 2.59) 1.45(0.65, 3.25) 1.54(0.75, 3.16) |
0.169 0.368 0.242 |
| Marital status | Single Married Divorced/Widowed Have boy/girl friend |
55(33.7) 29(26.1) 5(41.7) 11(28.9) |
108(66.3) 82(73.9) 7(58.3) 27(71.1) |
1.44(0.85, 2.46) Ref. 2.02(0.59, 6.86) 1.15(0.51, 2.61) |
0.180 0.260 0.735 |
| Have children | No Yes |
71(32.7) 29(26.9) |
146(67.3) 79(73.1) |
1.33(0.79, 2.21) Ref. |
0.281 |
| Academic status | Diploma Degree Others3 |
45(33.8) 50(29.8) 4(19.0) |
88(66.2) 118(70.2) 17(81.0) |
1.92(0.70, 5.24) 1.11(0.41,3.03) Ref. |
0.205 0.837 |
| Profession | Physician Nurse Pharmacist Lab. technologist Others4 |
4(26.7) 72(31.4) 9(31.0) 7(29.2) 6(25.0) |
11(73.3) 157(68.6) 20(69.0) 17(70.8) 18(75.0) |
1.09(0.25, 4.75) 1.38(0.52, 3.61) 1.35(0.40, 4.54) 1.24(0.35, 4.43) Ref. |
0.908 0.517 0.628 0.746 |
Note: 1Muslim, Jehovah, no religion and others (unlisted); 2Dawro, Keficho, Yem, Harari and siltie; 3Medical doctor (general doctor and specialist), master’s degree; 4Psychiatrist nurse, Physiotherapist, anesthetist nurse and sanitarian
Table 3: Bivariate logistic regression: Association of socio-demographic variables with mental distress among health professionals working in JUTH, December, 2013(n=334)
Prevalence of mental distress among staffs with history of physical and mental health problems was 46.2% (n=30) and 34.2 % (n=13) respectively.
Among the total study participants, 34.4% (n=115), 26.6% (n=89), 10.2% (n=34), 5.4% (n=18) were current alcohol drinkers, khat chewers, tobacco smokers and shisha users respectively. From the total participants, 14.4% (n=48) of them were found to have alcohol use disorder (CAGE≥2). Prevalence of mental distress among health professionals with alcohol use disorder was 50.0 % (n=24). Similarly, over all prevalence of mental distress among current khat users and current tobacco smokers was 44.0% (n=37) and 57.6% (n=19) respectively.
Socio-demographic factors
Prevalence of mental distress among divorced and widowed group of study participants was 41.7% (n=5) and it was 39.1% (n=59) among orthodox christians. Thirty four percent (n=31) Amhara and 26.5% (n=43) Oromo ethnic groups were found to have mental distress. Similarly, participants who had no child were showed slightly higher prevalence of mental distress (32.7%, n=71) when compared with those who had at least one child (26.9%, n=29).
Work demand variables
Mental distress was nearly three times higher among participants encountered verbal or physical abuse (42.7%, n=61) compared with their counterparts (21.1%, n=38). According to our result participants identified to have high score of burnout (above mean score of 43.39 ± 1.19) were showed higher prevalence of mental distress (51.8%, n=58) than participants scored below the mean.
Health related factors
Out of the total health professionals included in this study, 21% (n=60) and 11.4% (n=38) of them reported as they encountered a diagnosed physical and mental health problems respectively.
Participants with history of physical illness in the last one month showed 2.36 times higher prevalence of mental distress compared to their counterparts. Out of the total participants who reported idea of ending owns life, 79.17% (n=19) were with diagnosed mental health problems.
Substance related factors
Among the study participants 14.4% (n=48) of them were found to have alcohol use disorder (CAGE≥2). Participants with alcohol use disorder were identified to have 2.63 times higher prevalence of mental distress than their counterparts (50%, n=24). Current khat users and current tobacco smokers were similarly developed 2.22 times (44.0%, n=37) and 3.54 times (57.6%, n=19) higher prevalence of mental distress than non-users in bivariate analysis.
Multivariate logistic regression: Variables identified to have statistically significant associations with mental distress
After adjusting for potential confounders, using binary logistic regression analysis in which enter method (default) employed, it was found that poor prospect of promotion (AOR=2.08, 95% CI=1.05- 4.09), physical and verbal abuse (AOR= 2.34, 95% CI=1.23-2.37) and high score of burnout (AOR=4.47, 95%CI=2.37-8.44) were significantly independently associated with mental distress. The likelihood of developing mental distress among participants with high burnout score was 4.47 times more than participate with low burnout (AOR=4.47, 95%CI=2.37-8.44) in the final model. Additionally, the likelihood of developing mental distress among physically and verbally abused staffs was 2.34 times higher than their counterparts, also mental distress was more than two times higher among health professionals reported poor prospect of promotion than their peers claiming for good prospect of promotion (Table 4).
| Variables | Mental distress | Multiple logistic regression | |||
|---|---|---|---|---|---|
| Yes No (%) |
No No (%) |
AOR(95%CI) | p-value | ||
| Prospect ofPromotion | Good Poor |
33(21.4) 63(37.7) |
121(78.6) 104(62.3) |
Ref. 2.08(1.05, 4.09) |
0.035 |
| Physical/Verbal violence | Yes No |
61(42.7) 38(21.1) |
82(57.3) 142(78.9) |
Ref 0.36(0.17-0.77) |
0.009 |
| Level of burnout | High Low |
58(51.8) 41(19.6) |
54(48.2) 168(80.4) |
4.47(2.37, 8.44) Ref. |
<0.001 |
Table 4: Multivariate logistic regression: Variables identified to have statically significant associations with mental distress among health professionals working in JUTH, December, 2013 (n=334)
Nearly one-third (29.9%) of the study participants were identified to have mental distress which was similar with a study done in Jimma town, Ethiopia which found 25.8% prevalence of mental distress among community dwellers [23,24]. Also, it was similar with a study finding from Jimma prison, Ethiopia which found 35.9% prevalence of mental distress among homicide offenders and suspects [25].
Overall 29.9% prevalence of mental distress among health professionals from our study finding was found to be lower compared to similar study done in Iran among health professionals [26]. Health care insurance which is more practical in developed nations than in Ethiopian probably could have stressful conditions for health professionals. One the other hand the Iranian study used a different tool (GHQ) other than SRQ-20. Future research is needed to come up with a universal tool to detect mental distress. On the contrarily, it was found to be higher than similar study finding in Greece (20.7%) [11]. Also, it was higher than the study finding from Addis Ababa, Ethiopia which found 17.7% prevalence of mental distress among teachers and employees of commercial bank of Ethiopia [27]. The observed difference between our study finding and the studies above could be due to the gap in socio-demographic and socio-economic status among participants. Furthermore the nature of health care could have higher stress level compared with other professions (accountants) due to the long stay with sick people. Similarly difference in the cut-off point used with SRQ-20 items may probably explain the differences additionally. The cut-off point used to define mental distress in our study was used in previous studies in Ethiopia [23,25] (Figure 1).
Figure 1: Distribution of SRQ-20 item scores among health professionals working in JUTH, December 2013 (n=334)
Poor prospect of promotion was found to be independent predictor of mental distress. Meanwhile participants claimed poor prospect of promotion were 2.08 times more likely to have mental distress compared with participants reported good prospect of promotion (P-value=0.035, 95%CI=1.05-4.09). It was in agreement with study findings in United Kingdom, Australia and Brazil [28-30] which all showed poor chance of promotion for health professionals leave risk of mental distress.
The current study found physical/verbal abuse as independent predictor of mental distress (AOR=2.37, 95%CI=1.23-4.54). Those participants encountered physical/verbal abuse were 2.37 times more likely at risk to have mental distress than their counterparts. It was found consistent with a study report in Australian which found that, as score of work place violence decreased the chance of having mental distress was proportionally decreased [p=0.049, 95%CI=(-4.58 to -0.98)] among hospital nurses [31]. It was also in line with similar study finding in German among nurses found aggressive and depressive behavior of patients significantly associated with mental distress. Nurses’ who frequently faced challenging behavior by their patients were exposed for lower quality of general health and workability as well [32].
According to our result participants who identified to have high score of burnout (above the mean score of 43.39 ± 1.91) were 4.4 times more likely to have mental distress compared with their counterparts (AOR=4.40, 95%CI=2.66-7.29). It was consistent with the study done in German which found burnout as independent predictor of mental distress (p=0.009) [31]. Similarly, it was also consistent with the study done in Netherland which identified burnout (AOR=9.5, 95%CI=3.0- 30.6) as significantly associated with mental distress [30]. Based on these evidences, burnout is found to be crucial and it needs an immediate intervention of health professionals and stakeholders for wellbeing and functionality of staffs.
A case-control study in United Kingdom and a longitudinal study in USA found history of psychiatric disorders as predictor for mental distress; which is against our finding (p=0.019) [28,33]. Stigma against mental illness may influence our study finding [34]. As a result participants might have fear to report their past experience of mental illness. Additionally difference in study design of the two studies (casecontrol vs. cross-sectional) and time-line gap between the two study periods may also contribute for the gap.
Even though our study found higher prevalence of mental distress among participants with alcohol use disorder (50.0%, n=24) and tobacco smokers (57.6%, n=19), no significant association identified at multivariate logistic regression. It was againest with study findings in Ethiopia and Thailand, identified alcohol use disorder and tobacco smoking as predictors of mental distress [35,36]. Concurrent use of substances is common phenomenon in Ethiopia [27]. As a result using psycho-stimulants like khat in Ethiopia context may alleviate their distressing feelings. So that the difference might be availability and khat chewing habits of our participants (44%). Also other cofounders such as shisha smoking could have implications towards our finding.
Some important limitations must be considered when utilized our study results. This study didn’t assess use of benzodiazepines and oppoid drugs like pethidine which potentially used by health professionals because of its access in health care facilities.
The findings of this study doesn’t represent health professionals who are working both as lecturer of Jimma university and clinician in JUTH at the same time.
This study was comparable with different study findings. Probably the most important findings of our study are; high burnout score, poor prospect of promotion, and physical/verbal abuse were found to be independent predictors of mental distress. Prevalence of mental distress was higher among health professionals with history of physical health problems; however no difference in prevalence of mental distress found among participants with or without history of mental health problems. Most of the participants who had history mental health problems reported suicidal idea.
This study calls for prevention of stressful conditions, setting rules and regulations as well as strengthening staff communications to prevent harassment at work place. Similarly, health professionals need promotions and incentives due to the demanding nature of their job.
The authors declare that they have no competing interests
BA developed hypothesis, supervised data collection, analyzed and wrote drafts of the paper. EG and MS provided expert advice on methodology, analysis and report compilation of the study findings. KT assisted and advised in proposal development and in giving comment for the result finding. All authors approved the final manuscript submission.
This study was supported by Jimma University and JUTH. As stakeholder we would like to thank staffs of Jimma University and study participants. We would like to thank data collectors and department heads of JUTH for their welcome and assistance. Finally we would like acknowledge Jimma University, school of graduate studies for allowing us to conduct this study.