Understanding Lay Counselor Perspectives on Mobile Phone Supervision in Kenya: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Task shifting is an effective model for increasing access to mental health treatment via lay counselors with less specialized training that deliver care under supervision. Mobile phones may present a low-technology opportunity to replace or decrease reliance on in-person supervision in task shifting, but important technical and contextual limitations must be examined and considered. OBJECTIVE: Guided by human-centered design methods, we aimed to understand how mobile phones are currently used when supervising lay counselors, determine the acceptability and feasibility of mobile phone supervision, and generate solutions to improve mobile phone supervision. METHODS: Participants were recruited from a large hybrid effectiveness implementation study in western Kenya wherein teachers and community health volunteers were trained to provide trauma-focused cognitive behavioral therapy. Lay counselors (n=24) and supervisors (n=3) participated in semistructured interviews in the language of the participants' choosing (ie, English or Kiswahili). Lay counselor participants were stratified by supervisor-rated frequency of mobile phone use such that interviews included high-frequency, average-frequency, and low-frequency phone users in equal parts. Supervisors rated lay counselors on frequency of phone contact (ie, calls and SMS text messages) relative to their peers. The interviews were transcribed, translated when needed, and analyzed using thematic analysis. RESULTS: Participants described a range of mobile phone uses, including providing clinical updates, scheduling and coordinating supervision and clinical groups, and supporting research procedures. Participants liked how mobile phones decreased burden, facilitated access to clinical and personal support, and enabled greater independence of lay counselors. Participants disliked how mobile phones limited information transmission and relationship building between supervisors and lay counselors. Mobile phone supervision was facilitated by access to working smartphones, ease and convenience of mobile phone supervision, mobile phone literacy, and positive supervisor-counselor relationships. Limited resources, technical difficulties, communication challenges, and limitations on which activities can be effectively performed via mobile phone were barriers to mobile phone supervision. Lay counselors and supervisors generated 27 distinct solutions to increase the acceptability and feasibility of mobile phone supervision. Strategies ranged in terms of the resources required and included providing phones and airtime to support supervision, identifying quiet and private places to hold mobile phone supervision, and delineating processes for requesting in-person support. CONCLUSIONS: Lay counselors and supervisors use mobile phones in a variety of ways; however, there are distinct challenges to their use that must be addressed to optimize acceptability, feasibility, and usability. Researchers should consider limitations to implementing digital health tools and design solutions alongside end users to optimize the use of these tools. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s43058-020-00102-9.
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 | 0,006 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,012 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».