Evaluating a capacity building program on women’s health for displaced community health workers in fragile settings in Lebanon
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».