Human resources for health governance and leadership strategies for improving health outcomes in low- and middle-income countries: a narrative review
Bibliographic record
Abstract
BACKGROUND: Many low- and middle-income countries (LMICs) are facing a crisis of human resources for health (HRH) attributed to poor governance and leadership that characterizes the health sector in this setting. It is unclear which specific strategies are effective in ameliorating the crisis. METHODS: Selected electronic databases were searched up until 30 May 2020. Two authors screened studies independently and extracted data from included studies. Quality assessment was done using the Mixed Methods Appraisal Tool. Thematic analysis of the outcomes was done. RESULTS: We included 18 studies of variable designs across Africa, Asia, South America and the Pacific islands. Most were case-based studies and were of moderate to high quality. Several governance strategies with a positive impact on the health workforce and health outcomes identified included decentralization, central coordination and facilitation process, posting and transfer policies as well as the setting up of human resource units. CONCLUSIONS: Governance and leadership strategies targeting the HRH crises in LMIC are variable, interdependent and complex. While some show benefits in improving health workforce outcomes, only a few have an impact on population health outcomes.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".