Examining intersectoral collaboration among community health workers providing integrated maternal-child health and social care in resource-constrained settings in the Philippines
Bibliographic record
Abstract
Background: Reducing maternal-child health disparities in resource-constrained communities requires meaningful collaboration between different sectors to deliver integrated care. Community health workers (CHWs) are uniquely positioned within their communities, typically as volunteers, to act as an intersectoral bridge and catalyst for collaborative efforts to improve maternal and child health and care outcomes. While CHWs are widely recognized as crucial actors in the health and social care workforce, particularly in contexts with decentralized public health systems, a need exists to critically examine the strategies they employ to facilitate intersectoral collaboration and improve maternal and child health, with an eye to informing the expansion of these programs across resource-constrained contexts. Methods: This study was anchored by a partnership with a Philippines-based, non-governmental organization (International Care Ministries) and embedded within their ‘Community Health Champions’ (CHW) program, a program that trains and supports CHWs. In April 2023, CHWs from six locations in Negros Oriental, Philippines were recruited for 11 participatory focus groups (n=75 CHWs) and 64 semi-structured interviews. Data collection focused on strategies used by CHWs to collaborate across sectors to improve maternal and child health and social care. Focus groups and interviews were audio-recorded and transcribed. Transcripts were thematically analyzed using a hybrid inductive-deductive approach. Ethics approval was provided by the University of Waterloo, Canada (#44828). Results: CHWs (all female; ages 21-60) facilitated linkages between communities, non-governmental organizations, and the local public health system vis-à-vis working alongside public sector healthcare workers to identify individuals in need of support and to provide treatment or referral to formal care. This collaboration enabled a continuity of care, with CHWs viewing their role as addressing existing gaps within the public sector. Critically, CHWs' positionality and social networks held within communities shaped the degree and quality of intersectoral collaboration. The CHW volunteer role was one of many held by some participants (e.g., leader in a local savings group; employee within the municipality) which facilitated collaboration across sectors. Most CHWs were embedded within communities where they both lived and worked, and thus had expansive social networks to draw upon to facilitate intersectoral collaboration. All CHWs exhibited motivation to care for their communities, which shaped the overall quality of collaboration. Conclusion: This study highlights strategies used by CHWs as they embody and embed intersectoral collaboration in their efforts to enhance maternal and child health in resource-constrained settings in the Philippines. Opportunities exist to further amplify these efforts and support CHWs to act as a bridge across sectors. In particular, focused training and material resources could extend CHWs' impact in bridging communities, local health systems, and non-governmental organizations to improve maternal and child health and care outcomes. Within these efforts, further research is needed to examine and understand the role of social networks, trust, and pre-existing relationships in shaping the capacity of CHWs with respect to intersectoral collaboration and the delivery of integrated care.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".