Introduction to the Coproduction of Supervision Standards for Digital Peer Support: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Digital peer support enhances engagement in mental and physical health services despite barriers such as location, transportation, and other accessibility constraints. Digital peer support involves live or automated peer support services delivered through technology media such as peer-to-peer networks, smartphone apps, and asynchronous and synchronous technologies. Supervision standards for digital peer support can determine important administrative, educative, and supportive guidelines for supervisors to maintain the practice of competent digital peer support, develop knowledgeable and skilled digital peer support specialists, clarify the role and responsibility of digital peer support specialists, and support specialists in both an emotional and developmental capacity. OBJECTIVE: Although digital peer support has expanded recently, there are no formal digital supervision standards. The aim of this study is to inform the development of supervision standards for digital peer support and introduce guidelines that supervisors can use to support, guide, and develop competencies in digital peer support specialists. METHODS: Peer support specialists that currently offer digital peer support services were recruited via an international email listserv of 1500 peer support specialists. Four 1-hour focus groups, with a total of 59 participants, took place in October 2020. Researchers used Rapid and Rigorous Qualitative Data Analysis methods. Researchers presented data transcripts to focus group participants for feedback and to determine if the researcher's interpretation of the data match their intended meanings. RESULTS: We identified 51 codes and 11 themes related to the development of supervision standards for digital peer support. Themes included (1) education on technology competency (43/197, 21.8%), (2) education on privacy, security, and confidentiality in digital devices and platforms (33/197, 16.8%), (3) education on peer support competencies and how they relate to digital peer support (25/197, 12.7%), (4) administrative guidelines (21/197, 10.7%), (5) education on the digital delivery of peer support (18/197, 9.1%), (6) education on technology access (17/197, 8.6%), (7) supervisor support of work-life balance (17/197, 8.6%), (8) emotional support (9/197, 4.6%), (9) administrative documentation (6/197, 3%), (10) education on suicide and crisis intervention (5/197, 2.5%), and (11) feedback (3/197, 1.5%). CONCLUSIONS: Currently, supervision standards from the Substance Abuse and Mental Health Services Administration (SAMHSA) for in-person peer support include administrative, educative, and supportive functions. However, digital peer support has necessitated supervision standard subthemes such as education on technology and privacy, support of work-life balance, and emotional support. Lack of digital supervision standards may lead to a breach in ethics and confidentiality, workforce stress, loss of productivity, loss of boundaries, and ineffectively serving users who participate in digital peer support services. Digital peer support specialists require specific knowledge and skills to communicate with service users and deliver peer support effectively, while supervisors require new knowledge and skills to effectively develop, support, and manage the digital peer support role.
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 | 0,048 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,009 | 0,010 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».