Author comment: Addressing the mental health needs of healthcare professionals in Africa: a scoping review of workplace interventions — R1/PR7
Bibliographic record
Abstract
Healthcare workers in Africa face considerable stress due to factors like long working hours, heavy workloads and limited resources, leading to psychological distress. Generally, countries in the global north have well-established policies and employee wellness programs for mental health compared to countries in the global south. This scoping review aimed to synthesize evidence from published and grey literature on workplace mental health promotion interventions targeting African healthcare workers using Social Ecological Model (SEM) and the Job Demands-Resources (JD-R) model as an underlying theoretical framework for analysis. Arksey and O’Malley framework for scoping reviews was used. The search was conducted across multiple databases. A total of 5590 results were retrieved from Ovid MEDLINE, Ovid Embase, Ovid PsycINFO, Cochrane Library, CINAHL, Scopus and Web of Science. Seventeen (17) studies from ten (10) African countries were included after title, abstract and full text screening. Thematic analysis identified 5 key themes namely training programs, counselling services, peer support programs, relaxation techniques and informational resources. In conclusion, even though limited workplace mental health interventions for healthcare professionals were identified in Africa, individual-level interventions have been notably substantial in comparison to organizational and policy-level initiatives. Moving forward, a multi-faceted approach unique to the African context is essential.
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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 | 0.031 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".