Evaluating a capacity building program on women’s health for displaced community health workers in fragile settings in Lebanon
Bibliographic record
Abstract
BACKGROUND: Displaced populations in fragile settings experience health disparities that are seldom attended to. Task-shifting, which involves training non-specialized community health workers (CHW) to deliver basic education and health services is a favorable strategy to address this problem, however very little data exist on this topic in the Middle East region. We conducted a long-term evaluation of the Women's Health Certificate delivered to Syrian refugees and host community in informal tented settlements in Lebanon under the Mobile University for Health (MUH) program. The training was delivered through a mobile classroom approach that incorporated a blended learning modality. METHODS: We collected short-term data from the 42 trained CHW (knowledge assessments and satisfaction measures) during the delivery of the intervention between March and August 2019, and long-term data (semi-structured interviews with 8 CHW and focus group discussion with 9 randomly selected community members) one year later between July and August 2020. The evaluation approach was informed by the Kirkpatrick evaluation model, and the qualitative data were analyzed using qualitative content analysis. RESULTS: Data from the CHWs and community members were triangulated, and they showed that the training enhanced access to education due to its mobile nature and provided opportunities for CHWs to engage and interact with learning material that enhanced their knowledge and favorable behaviors regarding women's health. In turn, CHWs were empowered to play an active role in their communities to transfer the knowledge they gained through educating community members and providing women's health services and referrals. Community members benefited from the CHWs and called for the implementation of more similar training programs. CONCLUSION: This is one of few studies reporting a long-term community-level evaluation of a task-shifting program on women's health among displaced populations in Lebanon. Our findings support the need to increase funding for similar programs, and to focus on delivering programs for a variety of health challenges. It is also essential to enhance the reach and length of recruitment to wider communities, to design concise, interactive, and engaging sessions, and to provide tools to facilitate circulation of learning material, and resources for referrals to health services.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".