Exploring the Use of Multiple Participatory Tools to Engage Community Health Workers in Program Evaluation and Implementation: A Case Study From the Philippines
Bibliographic record
Abstract
This study explored the use of three participatory tools within a Philippines-based case study with community health workers (CHWs) by comparing and contrasting the process and data generated across the tools, and critically reflecting on adaptations and facilitation considerations that affected the tools' use. Facilitator notes and audio-recordings of discussions were integrated and analyzed thematically. Tools differed by the type of data generated: program-specific data related to CHWs' roles and responsibilities or data on broader structural factors. A stepwise approach within each tool facilitated focused, in-depth sharing, as did initial paired discussions that allowed exchange of knowledge and experiences among CHWs. Facilitators required topic- and context-specific knowledge to guide discussion effectively. CHWs discussed challenges and successes in their roles; program recommendations; and broader challenges related to healthcare delivery in their communities. This study contributes critical insights on the use of participatory tools to promote the inclusion of implementer perspectives in health program co-design, implementation, and evaluation.
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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 | 0.071 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| 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".