Health policy and system support to optimise community health worker programmes: an abridged WHO guideline
Bibliographic record
Abstract
Optimising community health worker (CHW) programmes requires evidence-based policies on their education, deployment, and management. This guideline aims to inform efforts by planners, policy makers, and managers to improve CHW programmes as part of an integrated approach to strengthen primary health care and health systems. The development of this guideline followed the standard WHO approach to developing global guidelines. We conducted one overview of reviews, 15 systematic reviews (each one on a specific policy question), and a survey of stakeholders' views on the acceptability and feasibility of the interventions under consideration. We assessed the quality of systematic reviews using the AMSTAR tool, and the certainty of the evidence using the GRADE methodology. The overview of reviews identified 122 eligible articles and the systematic reviews identified 137 eligible primary studies. The stakeholder perception survey obtained inputs from 96 respondents. Recommendations were developed in the areas of CHW selection, preservice education, certification, supervision, remuneration and career advancement, planning, community embeddedness, and health system support. These are the first evidence-based global guidelines for health policy and system support to optimise community health worker programmes. Key considerations for implementation include the need to define the role of CHWs in relation to other health workers and plan for the health workforce as a whole rather than by specific occupational groups; appropriately integrate CHW programmes into the general health system and existing community systems; and ensure internal coherence and consistency across different policies and programmes affecting CHWs.
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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 | 0.036 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".