The mental health toll among healthcare workers during the COVID-19 Pandemic in Malawi
Bibliographic record
Abstract
The COVID-19 pandemic has affected the mental health of healthcare workers worldwide, with frontline personnel experiencing heightened rates of depression, anxiety, and posttraumatic stress. This mixed-methods study aimed to assess the mental health toll of COVID-19 on healthcare workers in Malawi. A cross-sectional survey utilising the Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9), and Primary Care PTSD Screen for DSM-5 (PC-PTSD-5) was conducted among 109 frontline healthcare workers. Additionally, in-depth interviews were conducted with 16 healthcare workers to explore their experiences and challenges during the pandemic. The results indicated a high prevalence of COVID-19-related depression (31%; CI [23, 41]), anxiety (30%; CI [22, 40]), and PTSD (25%; CI [17, 34]) among participants. Regression analysis revealed significantly higher rates of depression, anxiety, and PTSD among healthcare workers in city referral hospitals compared to district hospitals. Qualitative findings highlighted the emotional distress, impact on work and personal life, and experiences of stigma and discrimination faced by healthcare workers. The stress process model provided a valuable framework for understanding the relationship among pandemic-related stressors, coping resources, and mental health outcomes. The findings underscore the urgent need for interventions and support systems to mitigate the mental health impact of COVID-19 on frontline healthcare workers in Malawi. Policymakers should prioritise the assessment and treatment of mental health problems among this critical workforce to maintain an effective pandemic response and build resilience for future crises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".