Psychological wellbeing in a resource-limited work environment: Examining levels and determinants among health workers in rural Malawi

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Study Justification:
– A competent, responsive, and productive health workforce is crucial for a well-performing health system.
– Psychological wellbeing of health workers is essential for their availability and productivity.
– Limited research exists on psychological wellbeing of health workers in resource-limited settings.
– This study aimed to fill the knowledge gap by investigating levels and factors associated with psychological wellbeing among mid-level health workers in rural Malawi.
Study Highlights:
– 25% of health workers had poor psychological wellbeing scores.
– Factors associated with psychological wellbeing included satisfaction with interpersonal relationships at work and not having received recent professional training.
– Other factors showed inconclusive results.
– The high proportion of health workers with poor wellbeing scores is concerning given the health workforce shortage in Malawi and the impact on work performance.
Study Recommendations:
– More research is needed to draw conclusions and provide recommendations on enhancing wellbeing.
– Psychological wellbeing should be considered a key concern for human resources for health.
Key Role Players:
– Health workers
– Health facility managers
– Ministry of Health officials
– Policy makers
– Researchers
Cost Items for Planning Recommendations:
– Training programs for health workers on psychological wellbeing
– Support for improving interpersonal relationships at work
– Research funding for further studies on wellbeing
– Resources for implementing recommendations in health facilities
– Monitoring and evaluation of interventions to enhance wellbeing
Please note that the cost items provided are general suggestions and not actual cost estimates.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are areas for improvement. The study provides a clear background and objective, and the methods are described in detail. The results are presented, but some associations are inconclusive. To improve the evidence, the study could include a larger sample size and conduct further analysis to strengthen the associations between psychological wellbeing and the factors examined. Additionally, validating the WHO-5 Wellbeing Index in the specific context of Malawi would enhance the reliability of the findings.

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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Based on the provided information, it seems that the study focused on assessing the psychological wellbeing of mid-level health workers in rural Malawi. The study aimed to identify factors associated with poor psychological wellbeing and highlight the importance of considering this as a key concern for human resources for health. However, the study did not specifically mention innovations or recommendations to improve access to maternal health. Therefore, based on the information provided, it is not possible to provide specific innovations for improving access to maternal health.
AI Innovations Description
Based on the information provided, the recommendation to improve access to maternal health would be to prioritize the psychological well-being of mid-level health workers in rural Malawi. This can be achieved through the following actions:

1. Increase support for interpersonal relationships at work: The study found a positive association between health workers’ satisfaction with interpersonal relationships at work and their psychological well-being. Therefore, efforts should be made to foster a supportive and positive work environment, including promoting teamwork, communication, and collaboration among health workers.

2. Provide regular professional training: The study found a negative association between health workers’ psychological well-being and having received professional training recently. This suggests that ongoing professional development and training opportunities should be provided to health workers to enhance their skills and knowledge, which can contribute to their overall well-being.

3. Address structural challenges in the health system: The study highlighted various structural challenges in the Malawian health system, such as severe health worker shortages, management and supervision issues, and stock-outs of essential supplies. Addressing these challenges is crucial to alleviate the workload and stress experienced by health workers, ultimately improving their psychological well-being.

4. Improve remuneration and career development opportunities: The study mentioned that health workers in Malawi are frustrated with low salary levels, delays in payment, and limited career development opportunities. Efforts should be made to ensure fair and timely remuneration for health workers, as well as provide clear pathways for career advancement and professional growth.

5. Recognize and appreciate health workers’ efforts: Health workers expressed a lack of recognition for their efforts and good performance. Implementing mechanisms to acknowledge and appreciate the work of health workers, such as performance-based incentives or recognition programs, can contribute to their psychological well-being.

Overall, prioritizing the psychological well-being of health workers in rural Malawi can lead to improved access to maternal health by ensuring a competent, responsive, and productive health workforce.
AI Innovations Methodology
Based on the provided description, the study focuses on the psychological wellbeing of mid-level health workers in rural Malawi. To improve access to maternal health, the following innovations and recommendations can be considered:

1. Mental health support programs: Implementing mental health support programs specifically tailored for health workers can help address the psychological challenges they face. These programs can include counseling services, stress management workshops, and peer support groups.

2. Training and capacity building: Providing training and capacity building opportunities for health workers can enhance their skills and knowledge in maternal health care. This can include workshops, seminars, and online courses on topics such as obstetric care, emergency response, and communication skills.

3. Improved working conditions: Enhancing the physical work environment, ensuring adequate resources and supplies, and addressing issues such as workload and staffing shortages can contribute to better psychological wellbeing among health workers. This can be achieved through infrastructure improvements, increased staffing, and better management practices.

4. Recognition and incentives: Recognizing the efforts and achievements of health workers through incentives, rewards, and career development opportunities can boost their morale and job satisfaction. This can include performance-based bonuses, promotions, and opportunities for professional growth.

To simulate the impact of these recommendations on improving access to maternal health, a methodology can be developed as follows:

1. Define indicators: Identify key indicators that reflect access to maternal health, such as the number of antenatal care visits, skilled birth attendance, and postnatal care utilization.

2. Data collection: Collect baseline data on the selected indicators from the target population, including health facilities and pregnant women. This can be done through surveys, interviews, and record reviews.

3. Intervention implementation: Implement the recommended innovations and interventions in selected health facilities. This can involve training programs, infrastructure improvements, and incentive schemes.

4. Data analysis: Analyze the post-intervention data on the selected indicators and compare them with the baseline data. This can be done using statistical methods such as regression analysis or chi-square tests to assess the impact of the interventions on improving access to maternal health.

5. Evaluation and interpretation: Evaluate the results of the data analysis and interpret the findings. Assess the extent to which the recommended innovations have contributed to improving access to maternal health. Identify any limitations or challenges encountered during the simulation process.

6. Recommendations and scaling up: Based on the evaluation findings, provide recommendations for scaling up the successful interventions to a larger population or replicating them in other settings. Consider the feasibility, cost-effectiveness, and sustainability of the interventions for broader implementation.

By following this methodology, researchers and policymakers can gain insights into the potential impact of the recommended innovations on improving access to maternal health and make informed decisions on implementing them on a larger scale.

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