“I am the bridge”: Examining intersectoral collaboration among community health workers to address maternal and child health in the Philippines
Bibliographic record
Abstract
Community health workers (CHWs) are uniquely positioned to act as a bridge between local maternal and child health needs and the broader health system. However, there is a need to examine the specific strategies CHWs use to facilitate intersectoral collaboration and support community-level maternal and child health service delivery. This study was conducted in partnership with a Philippines-based NGO and their CHW program. In total, 64 semi-structured interviews were conducted with CHWs from six locations in Negros Oriental, Philippines. Data collection focused on CHWs’ efforts to address maternal and child health and collaborate across sectors to support health service delivery. Qualitative data were analyzed with a hybrid inductive-deductive approach. CHWs (all females; ages 21–60) leveraged the multiple roles and social networks they held, including with local health system and government actors, to address maternal and child health. CHWs viewed their role as addressing service gaps and providing continuity of care with the public health system (service extenders); liaising between communities and both the NGO and public sector to support service navigation (cultural brokers); and working to address complex social and ecological determinants of health within their communities (social change agents). This study provides insights into how NGO-public sector collaboration is facilitated by CHWs to support maternal and child health in communities. In addition, this study demonstrates how broader health system governance arrangements and decentralization may impact the experiences and roles of CHWs affiliated with NGO-led programs. • Community health workers can facilitate health system accountability in communities. • We examined the efforts of NGO-affiliated CHWs to support maternal and child health. • CHWs filled service gaps, brokered community trust, and exceeded prescribed roles. • The positionality and social networks of CHWs shaped intersectoral collaboration. • Health systems governance can influence NGO-public sector collaboration.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.011 |
| 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".