Governance of community health worker programs in a decentralized health system: a qualitative study in the Philippines
Bibliographic record
Abstract
Abstract Background Community health worker (CHW) programs are an important resource in the implementation of universal health coverage (UHC) in many low- and middle-income countries (LMICs). However, in countries with decentralized health systems like the Philippines, the quality and effectiveness of CHW programs may differ across settings due to variations in resource allocation and local politics. In the context of health system decentralization and the push toward UHC in the Philippines, the objective of this study was to explore how the experiences of CHWs across different settings were shaped by the governance and administration of CHW programs. Methods We conducted 85 semi-structured interviews with CHWs ( n = 74) and CHW administrators ( n = 11) in six cities across two provinces (Negros Occidental and Negros Oriental) in the Philippines. Thematic analysis was used to analyze the qualitative data with specific attention to how the experiences of participants differed within and across geographic settings. Results Health system decentralization contributed to a number of variations across settings including differences in the quality of human resources and the amount of financial resources allocated to CHW programs. In addition, the quality and provider of CHW training differed across settings, with implications for the capacity of CHWs to address specific health needs in their community. Local politics influenced the governance of CHW programs, with CHWs often feeling pressure to align themselves politically with local leaders in order to maintain their employment. Conclusions The functioning of CHW programs can be challenged by health system decentralization through the uneven operationalization of national health priorities at the local level. Building capacity within local governments to adequately resource CHWs and CHW programs will enhance the potential of these programs to act as a bridge between the local health needs of communities and the public health system.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".