Process evaluation of the systematic medical appraisal, referral and treatment (SMART) mental health project in rural India
Notice bibliographique
Résumé
BACKGROUND: Availability of basic mental health services is limited in rural areas of India. Health system and individual level factors such as lack of mental health professionals and infrastructure, poor awareness about mental health, stigma related to help seeking, are responsible for poor awareness and use of mental health services. We implemented a mental health services delivery model that leveraged technology and task sharing to facilitate identification and treatment of common mental disorders (CMDs) such as stress, depression, anxiety and suicide risk in rural areas of the state of Andhra Pradesh, India. The intervention was delivered by lay village health workers (Accredited Social Health Activists - ASHAs) and primary care doctors. An anti-stigma campaign was implemented prior to this activity. This paper reports the process evaluation of the intervention using mixed methods. METHODS: A mixed methods pre-post evaluation assessed the intervention using quantitative service usage analytics from the server, and qualitative interviews with different stakeholders. Barriers and facilitators in implementing the intervention were identified. RESULTS: Health service use increased significantly at post-intervention, ASHAs could followup 78.6% of those who had screened positive, and 78.6% of the 1243 Interactive Voice Response System calls made, were successful. Most respondents were aware of the intervention. They indicated that knowledge received through the intervention empowered them to approach ASHAs and share their mental health symptoms. ASHAs and doctors opined that EDSS was useful and easy to use. Medical camps organized in villages to increase access to the doctor were received positively by all. However, some aspects or facilitators of the intervention need to be improved, including network connectivity, booster training, anti-stigma campaigns, quality of mental health services provided by doctors, provision of psychotropic medications at primary health centers and frequency of health camps. CONCLUSION: The respondents' views helped to understand the barriers and facilitators for improving the likely effectiveness of the intervention using Andersen's Modified Behavioral Model of Health Services Use, and identify the mechanisms by which those factors affected mental health services uptake in the community. TRIAL REGISTRATION: The study is registered with Clinical Trials Registry India (Applied - 16/07/14-Ref2014/07/007256; registration received - 04/10/17-CTRI/2017/10/009992 ).
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 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 | 0,055 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».