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Background: A competent, responsive, and productive health workforce is central to a well-performing health system capable of providing universal access to high-quality care. Ensuring health workers’ psychological wellbeing is critical to sustaining their availability and productivity. This is particularly true in heavily constrained health systems in low- and lower-middle-income countries. Research on the issue, however, is scarce. This study aimed to contribute to filling the gap in knowledge by investigating levels of and factors associated with psychological wellbeing of mid-level health workers in Malawi. Methods: The study relied on a cross-sectional sample of 174 health workers from 33 primary- and secondary-level health facilities in four districts of Malawi. Psychological wellbeing was measured using the WHO-5 Wellbeing Index. Data were analyzed using linear and logistic regression models. Results: Twenty-five percent of respondents had WHO-5 scores indicative of poor psychological wellbeing. Analyses of factors related to psychological wellbeing showed no association with sex, cadre, having dependents, supervision, perceived coworker support, satisfaction with the physical work environment, satisfaction with remuneration, and motivation; a positive association with respondents’ satisfaction with interpersonal relationships at work; and a negative association with having received professional training recently. Results were inconclusive in regard to personal relationship status, seniority and responsibility at the health facility, clinical knowledge, perceived competence, perceived supervisor support, satisfaction with job demands, health facility level, data collection year, and exposure to performance-based financing. Conclusions: The high proportion of health workers with poor wellbeing scores is concerning in light of the general health workforce shortage in Malawi and strong links between wellbeing and work performance. While more research is needed to draw conclusions and provide recommendations as to how to enhance wellbeing, our results underline the importance of considering this as a key concern for human resources for health.
The study took place in four rural health districts in Central and Southern Malawi, Balaka, Dedza, Ntcheu, and Mchinji. Despite substantial progress on various health indicators in recent years, the country continues to face a high mortality and morbidity burden due to communicable, non-communicable, and maternity-related conditions [27]. The Malawian health system is a predominantly public, government-funded three-tier system providing essential healthcare services to patients free of charge [28]. Health care service utilization is high [27], but provision of quality care is challenged by high workload levels due to severe health worker shortages, challenges in management and supervision, frequent stock-outs of drugs and other essential supplies, and other structural challenges [28–30]. Health workers are further frustrated with low salary levels and delays in payment thereof, limited and non-transparent career development opportunities, and lack of recognition of effort and good performance, as well as a variety of other factors [30, 31]. Despite working in difficult environments, Malawian health workers have expressed high levels of intrinsic motivation, pride in their work, and feelings of duty and of importance of their job in previous research [30, 32, 33]. The study used data collected within the context of the impact evaluation of the Results-based Financing for Maternal and Newborn Care (RBF4MNH) Initiative, implemented in the country between 2013 and 2018. The impact evaluation covered 28 primary-level and five secondary-level health facilities providing emergency obstetric care across the four study districts (eight or nine facilities per district). The selection of intervention and comparison health facilities is described in detail elsewhere [34]. Data was collected from all 33 facilities just before (March/April 2013) and approximately 2 years (June/July 2015) after the start of RBF4MNH. For the purpose of this study, we pooled the 2013 and 2015 data. The role of RBF4MNH is not the focus in this study, but we controlled for time of data collection and RBF4MNH exposure (i.e., working in an RBF4MNH facility) in all analyses. At health worker level, in all 33 study facilities, a repeated cross-sectional survey was performed in 2013 and 2015. Data were collected using a structured survey, administered face-to-face by trained interviewers with the support of tablet computers, in English which is the working language in Malawi. All health workers providing maternal health care services (i.e., clinical officers, medical assistants, registered/enrolled nurse/midwives, nurse-midwife technicians) who had worked at the health facility for at least 3 months and who were available at the time of data collection were sampled. In total, 174 health workers were interviewed, 74 in 2013 and 100 at 2015. Due to frequent turnover of staff in the Malawian setting and the rotational nature of service organization, only 10% of health workers were interviewed both in 2013 and 2015. Table 1 provides an overview over the sample and key demographic characteristics. Sample characteristics Psychological wellbeing of health workers was measured using the WHO-5 Wellbeing Index (abbreviated as “WHO-5” in the following), a short, disease-unspecific, and non-invasive self-rating scale [35, 36] (see Table 2). The WHO-5 has been translated into over 30 languages and used vastly in a wide range of fields of application, although with health workers in a LLMIC only in the study in Zimbabwe mentioned earlier, where it was not validated [22]. Despite this lack of context-specific validation studies, we have no reason for serious doubts in its cross-cultural validity due to the straightforward language and item wording which does not appear to be particularly sensitive to cultural norms [36]. Both Cronbach’s α (.72) and factor analysis results (Loevinger H = .380, p = 0.000) support the notion that the WHO-5 items measure a unidimensional wellbeing factor. WHO-5 Wellbeing Index [35] Scoring: The raw score is calculated by summing the points associated with the answers to the five statements. The raw score therefore ranges from 0 to 15, 0 representing the worst possible and 15 the best possible wellbeing. For the analyses, the raw score was linear transformed to decimal values between 0 and 1, corresponding to percentage of maximum score A number of studies primarily in high-income settings have further shown the usefulness, validity, and sensitivity of the WHO-5 as a screening tool for mental illness. Based on this research, WHO-5 scores below 50% of the maximum score (i.e., below 8 on the 0–15 range) are considered indicative of potentially clinically relevant mental health problems. If the WHO-5 is used as a mental health screening tool, it is recommended that individuals scoring below this threshold undergo more intensive testing for mental illness [36]. We are not aware of any studies investigating the validity of this threshold in LLMIC generally or in sub-Saharan Africa more specifically. We used the WHO-5 both in continuous form—to reflect our main conceptualization of PW as a continuum—and in dichotomized form along the 50% threshold to determine the proportion of the sample with WHO-5 scores indicative of potentially clinically relevant poor PW. To address the issue of lack of context-specific validation of the 50% threshold, we performed additional sensitivity analyses moving the threshold to (approximately) 40% (below 6 on the 0–15 range) and 60% (below 10). Table 3 provides an overview of potential individual-level characteristics associated with PW, as well as details on measurement for non-standard variables. The choice of variables resulted from joint consideration of the conceptual framework presented in the introduction, and availability of respective variables in the questionnaire. Explanatory variables and their measurement Yes No 44.8% 55.2% Yes No 77.0% 23.0% Yes No 57.5% 42.5% Mean sd 0.59 0.24 Mean sd 0.86 0.12 Yes No 58.1% 41.9% Mean sd 0.64 0.18 Mean sd 0.75 0.14 Mean sd 0.48 0.29 Mean sd 0.18 0.27 Mean sd 0.50 0.27 Mean sd 0.72 0.17 Mean sd 0.77 0.14 Mean sd 0.50 0.29 Note: Responses to Likert items (marked *) were given on a scale from 1 (strongly disagree/unsatisfied) to 5 (strongly agree/very satisfied). For variables measured with more than one Likert item, the unweighted mean of responses to all items was calculated. At the analytical level, all variables measured with multiple items were rescaled to range from 0 (lowest level) to 1 (highest level) for ease of interpretation In a first step, we performed χ2 tests for subsample differences in PW on key variables. We then employed linear (continuous outcome) and logistic (dichotomous outcome) regression models with standard errors clustered at facility level to determine the strength of association of the individual-level factors in Table 3 with PW. Data were complete for the WHO-5. For the predictor variables, data were missing for less than 2% of the sample for all variables except age (3.5%) and were imputed using modes/means in the respective RBF4MNH impact evaluation study arm*data collection year subsample.
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