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)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».