Barriers and drivers to capacity-building in global mental health projects
Notice bibliographique
Résumé
BACKGROUND: The global shortage of mental health workers is a significant barrier to the implementation and scale-up of mental health services. Partially as a result of this shortage, approximately 85% of people with mental, neurological and substance-use disorders in low- and middle-income countries do not receive care. Consequently, developing and implementing scalable solutions for mental health capacity-building has been identified as a priority in global mental health. There remains limited evidence to inform best practices for capacity building in global mental health. As one in a series of four papers on factors affecting the implementation of mental health projects in low- and middle-income countries, this paper reflects on the experiences of global mental health grantees funded by Grand Challenges Canada, focusing on the barriers to and drivers of capacity-building. METHODS: Between June 2014 and May 2017, current or former Grand Challenges Canada Global Mental Health grantees were recruited using purposive sampling. N = 29 grantees participated in semi-structured qualitative interviews, representing projects in Central America and the Caribbean (n = 4), South America (n = 1), West Africa (n = 4), East Africa (n = 6), South Asia (n = 11) and Southeast Asia (n = 3). Based on the results of a quantitative analysis of project outcomes using a portfolio-level Theory of Change framework, six key themes were identified as important to implementation success. As part of a larger multi-method study, this paper utilized a framework analysis to explore the themes related to capacity-building. RESULTS: Study participants described barriers and facilitators to capacity building within three broad themes: (1) training, (2) supervision, and (3) quality assurance. Running throughout these thematic areas were the crosscutting themes of contextual understanding, human resources, and sustainability. Additionally, participants described approaches and mechanisms for successful capacity building. CONCLUSIONS: This study demonstrates the importance of capacity building to global mental health research and implementation, its relationship to stakeholder engagement and service delivery, and the implications for funders, implementers, and researchers alike. Investment in formative research, contextual understanding, stakeholder engagement, policy influence, and integration into existing systems of education and service delivery is crucial for the success of capacity building efforts.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».