Designing an App to Support Measurement-Based Peer Supervision of Frontline Health Workers Delivering Brief Psychosocial Interventions in Texas: Multimethod Study
Notice bibliographique
Résumé
BACKGROUND: The unmet need for mental health care affects millions of Americans. A growing body of evidence in implementation science supports the effectiveness of task sharing in the delivery of brief psychosocial interventions. The digitization of training and processes supporting supervision can rapidly scale up task-shared interventions and enable frontline health workers (FLWs) to learn, master, and deliver interventions with quality and support. OBJECTIVE: We aimed to assess the perceived feasibility and acceptability of a novel mobile and web app designed and adapted to support the supervision, training, and quality assurance of FLWs delivering brief psychosocial interventions. METHODS: We followed human-centered design principles to adapt a prototype app for FLWs delivering brief psychosocial interventions for depression, drawing from an app previously designed for use in rural India. Using a multimethod approach, we conducted focus group sessions comprising usability testing and group interviews with FLWs recruited from a large health system in Texas to assess the feasibility and acceptability of the app. The positive System Usability Scale was used to determine the app's overall usability. We also calculated the participants' likelihood of recommending the app to others using ratings of 0 to 10 from least to most likely (net promoter score). Focus group transcripts were coded and analyzed thematically, and recommendations were summarized across 4 key domains. RESULTS: A total of 18 FLWs varying in role and experience with client care participated in the study. Participants found the app to be usable, with an average System Usability Scale score of 72.5 (SD 18.1), consistent with the industry benchmark of 68. Participants' likelihood of recommending the app ranged from 5 to 10, yielding a net promoter score of 0, indicating medium acceptability. Overall impressions of the app from participants were positive. Most participants (15/18, 83%) found the app easy to access and navigate. The app was considered important to support FLWs in delivering high-quality mental health care services. Participants felt that the app could provide more structure to FLW training and supervision processes through the systematic collection and facilitation of performance-related feedback. Key concerns included privacy-related and time constraints regarding implementing a separate peer supervision mechanism that may add to FLWs' workloads. CONCLUSIONS: We designed, built, and tested a usable, functional mobile and web app prototype that supports FLW-delivered psychosocial interventions in the United States through a structured supervision mechanism and systematic collection and review of performance measures. The app has the potential to scale the work of FLWs tasked with delivering these interventions to the hardest-to-reach communities they serve. The results of this project will inform future work to evaluate the app's use and efficacy in real-world settings to support task-shared mental health programs across the United States.
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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 | 0,010 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».