SMART Mental Health Project: process evaluation to understand the barriers and facilitators for implementation of multifaceted intervention in rural India
Notice bibliographique
Résumé
BACKGROUND: Globally, mental health problems are a growing public health concern. Resources and services for mental disorders are disproportionately low compared to disease burden. In order to bridge treatment gaps, The Systematic Medical Appraisal, Referral and Treatment (SMART) Mental Health Project was implemented across 12 villages in West Godavari district of the southern Indian state of Andhra Pradesh. This paper reports findings from a process evaluation of feasibility and acceptability of the intervention that focused on a mental health services delivery model to screen, diagnose and manage common mental disorders (CMDs). METHODS: A mixed methods evaluation was undertaken using quantitative service usage analytics, and qualitative data from in-depth interviews and focus group discussions were conducted with stakeholders including primary care physicians, community health workers, field staff and community members. Barriers to and facilitators of intervention implementation were identified. Andersen's Behavioral Model for Health Services Use was the conceptual framework used to guide the process evaluation and interpretation of data. RESULTS: In all, 41 Accredited Social Health Activists (ASHAs) and 6 primary health centre (PHC) doctors were trained in mental health symptoms and its management. ASHAs followed up 98.7% of screen positive cases, and 81.2% of these were clinically diagnosed and treated by the PHC doctors. The key facilitators of implementation were adequate training and supervision of field staff, ASHAs and doctors, use of electronic decision support, incorporation of a door-to-door campaign and use of culturally tailored dramas/videos to raise awareness about CMDs, and organising health camps at the village level facilitating delivery of intervention activities. Barriers to implementation included travel distance to receive care, limited knowledge about mental health, high level of stigma related to mental health issues, and poor mobile network signals and connectivity in the villages. Lack of familiarity with and access to mobile phones, especially among women, to accessing health related messages as part of the intervention. CONCLUSIONS: The evaluation not only provides a context to the interventions delivered, but also allowed an understanding of possible factors that need to be addressed to make the programme scalable and of benefit to policy makers.
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 | 0,003 | 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 ».