Essential aid made fully visible: understanding the proCHW financing landscape analysing accessible donor data sources
Bibliographic record
Abstract
INTRODUCTION: Community health workers (CHWs) play a critical role in extending healthcare services to underserved populations, especially in low-income and middle-income countries. Professional CHWs (proCHWs), who are salaried, skilled, supplied and supervised, are essential for achieving Universal Health Coverage and other global health goals. Despite the growing recognition of proCHWs, there is limited understanding of the global financing landscape for these workers. This study analyses the availability of data detailing the allocation of funding from major global development organisations for proCHWs. METHODS: The study was conducted by the Community Health Impact Coalition (CHIC) using a two-stage approach. First, eight major global funders were selected through a consultative process with CHIC members, chosen based on their perceived influence, leadership in community health and scale of financial commitments. The second stage involved mapping and analysing the funding availability of these organisations through desk reviews, brief consultations and analysis of public funding databases. The transparency of proCHW-specific funding data was assessed using a classification system: 'yes' (full availability), 'partial' (moderate availability) and 'no' (low/no availability). RESULTS: The analysis revealed a gap in accessible data required to quantify the funding for CHWs, particularly proCHWs, across the eight organisations. Only two organisations, The Global Fund and the President's Malaria Initiative, provided partial data visibility, while none fully disclosed specific funding amounts for proCHW programmes. Most organisations did not systematically track or report CHW investments, making it challenging to assess global funding flows. CONCLUSIONS: The study highlights gaps in the availability of data related to funding for proCHWs, hampering the ability to track and evaluate investments in proCHW programmes. The study recommends global funders improve the specificity of their data reporting and integrate proCHW indicators into standard reporting tools. Enhanced data reporting is essential for optimising investments in proCHW programmes and advancing global health equity.
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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 | 0.086 | 0.252 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".