Capacity building and community of practice for women community health workers in low-resource settings: long-term evaluation of the Mobile University For Health (MUH)
Bibliographic record
Abstract
Background: Lebanon has been facing a series of crises, significantly increasing health challenges, and straining its healthcare infrastructure. This caused deficiencies in the system's ability to attend to population health needs, and it profoundly impacted vulnerable and refugee communities who face additional challenges accessing healthcare services. In response, the Global Health Institute at the American University of Beirut designed and implemented the Mobile University for Health (MUH), which promotes task-shifting through capacity building complemented by communities of practice (CoP). The program aimed to prepare vulnerable women to assume the role of community health workers (CHW) within their communities, and to promote positive health knowledge and behaviours. Methods: A mixed-methods approach was used to evaluate MUHs' three certificates (women's health, mental health and psychosocial support, and non-communicable diseases). Implementation took place between 2019 and 2022, with 83 CHWs graduating from the program. Short-term data including knowledge assessments, course evaluations, and community member feedback surveys were collected. 93 semi-structured interviews with CHWs and 14 focus group discussions with community members were conducted to evaluate the long-term impact of the capacity building and CoP components. Results: Data revealed multiple strengths of the initiative, including increased access to education for the community, effectiveness of blended learning modality, successful planning and delivery of CoP sessions, and improved knowledge, skills, and health behaviours over time. The supplementary CoP sessions fostered trust in CHWs, increased community empowerment, and increased leadership skills among CHWs. However, some challenges persisted, including limited access to healthcare services, implementation logistical issues, difficulties with some aspects of the learning modality, and some resistance within the communities. Conclusion: MUH promoted and improved positive health knowledge and behaviours within targeted vulnerable populations in Lebanon. The supplementary CoP component proved instrumental in empowering CHWs and enhancing their impact within their communities. The study highlights the need for ongoing training and support for CHWs and underscores the importance of continued investment and adaptation of such initiatives through a gendered lens. This evaluation provides evidence on the successes of a capacity building model that has strong potential for scale and replication across health topics in conflict-affected contexts.
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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 | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".