Considering culture, context and community in mhGAP implementation and training: challenges and recommendations from the field
Notice bibliographique
Résumé
BACKGROUND: Major efforts are underway to improve access to mental health care in low- and middle-income countries (LMIC) including systematic training of non-specialized health professionals and other care providers to identify and help individuals with mental disorders. In many LMIC, this effort is guided by the mental health Gap Action Programme (mhGAP) established by the World Health Organization, and commonly centres around one tool in this program: the mhGAP-Intervention Guide. OBJECTIVE: To identify cultural and contextual challenges in mhGAP training and implementation and potential strategies for mitigation. METHOD: An informal consultative approach was used to analyze the authors' combined field experience in the practice of mhGAP implementation and training. We employed iterative thematic analysis to consolidate and refine lessons, challenges and recommendations through multiple drafts. Findings were organized into categories according to specific challenges, lessons learned and recommendations for future practice. We aimed to identify cross-cutting and recurrent issues. RESULTS: Based on intensive fieldwork experience with a focus on capacity building, we identify six major sets of challenges: (i) cultural differences in explanations of and attitudes toward mental disorder; (ii) the structure of the local health-care system; (iii) the level of supervision and support available post-training; (iv) the level of previous education, knowledge and skills of trainees; (v) the process of recruitment of trainees; and (vi) the larger socio-political context. Approaches to addressing these problems include: (1) cultural and contextual adaptation of training activities, (2) meaningful stakeholder and community engagement, and (3) processes that provide support to trainees, such as ongoing supervision and Communities of Practice. CONCLUSION: Contextual and cultural factors present major barriers to mhGAP implementation and sustainability of improved services. To enable trainees to effectively apply their local cultural knowledge, mhGAP training needs to: (1) address assumptions, biases and stigma associated with mental health symptoms and problems; (2) provide an explicit framework to guide the integration of cultural knowledge into assessment, treatment negotiation, and delivery; and (3) address the specific kinds of problems, modes of clinical presentations and social predicaments seen in the local population. Continued research is needed to assess the effectiveness these strategies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 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 | 0,000 | 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 ».