Amplifying Community Health Worker Voices to Examine the Implementation and Scaling of a Community Health Program: A Qualitative Study in the Philippines
Bibliographic record
Abstract
Background: Scaling community-based health programs can contribute to efforts to achieve universal health coverage. Implementers of community-based health programs, such as community health workers (CHWs), hold valuable insights and experiences that can inform how these programs scale up and out. Objectives: (1) To assess implementer experiences and perceptions of a community-based health program delivered by CHWs that underwent recent changes to broaden its programing and reach (ie, scaling up); and (2) to describe facilitators and barriers to the implementation of this community-based health program to inform subsequent scaling out. Methods: In April 2023, an evaluation of an NGO-led CHW program was conducted with program implementers across 6 geographic regions in Negros Oriental, Philippines (n = 64 semi-structured interviews). Data were analyzed using a hybrid inductive-deductive analysis, informed by the Medical Research Council's framework for process evaluation of complex interventions. Ethics approval was provided by the University of Waterloo Research Ethics Board (Certificate #: 44828). Results: CHWs perceived that the scaled up version of the program was more useful and impactful, given its expanded reach and provision of basic treatment; however, new program components also required significant time, effort, and strategy to implement which created new opportunity costs for CHWs. Implementation of the scaled up version of the program was facilitated via clear communication structures and supportive group training spaces. Pre-existing roles and social networks held by CHWs further facilitated the implementation of the scaled up version of the program. Overall, new individual-level and community-based strategies were leveraged by CHWs to implement the scaled up program, and participants described facilitators (eg, expanded reach) and barriers (eg, increased time and effort) that influenced program implementation. Conclusion: This study contributes insights into how individuals involved in the scaling of a community-based health program may experience this process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".