Group Cohesion and Necessary Adaptations in Online Hearing Voices Peer Support Groups: Qualitative Study With Group Facilitators
Notice bibliographique
Résumé
BACKGROUND: Face-to-face hearing voices peer support groups (HVGs), a survivor-led initiative that enables individuals who hear voices to engage with the support of peers, have a long-standing history in community settings. HVGs are premised on the notion that forming authentic, mutual relationships enables the exploration of one's voice hearing experiences and, in turn, reduces subjective distress. As such, group cohesion is assumed to be a central mechanism of change in HVGs. The rise of digital mental health support, coupled with the COVID-19 pandemic, has resulted in many HVGs adapting to online delivery. However, to date no studies have examined the implementation of these online groups and the adaptations necessary to foster cohesion. OBJECTIVE: This study aims to understand the experience of group cohesion among HVG facilitators in online groups compared with face-to-face groups. Specifically, we examined the ways in which the medium through which groups run (online or face-to-face) impacts group cohesion and how facilitators adapted HVGs to foster group cohesion online. METHODS: Semistructured qualitative interviews were conducted with 11 facilitators with varied experience of facilitating online and face-to-face HVGs. Data were analyzed using reflexive thematic analysis. RESULTS: The findings are organized into 3 themes and associated subthemes: nonverbal challenges to cohesion (lack of differentiation, transitional space, inability to see the whole picture, and expressions of empathy); discursive challenges to cohesion (topic-based conversation and depth of disclosure); and necessary adaptations for online groups (fostering shared experience and using the unique context to demonstrate investment in others). Despite challenges in both the setting and content of online groups, facilitators felt that group cohesion was still possible to achieve online but that it had to be facilitated intentionally. CONCLUSIONS: This study is the first to specifically investigate group cohesion in online HVGs. Participants noted numerous challenges to group cohesion when adapting groups to run online, including the unnaturally linear narrative flow of dialogue in online settings; lack of transitional spaces, and associated small talk before and after the session; ease of disengagement online; inhibited sharing; and absence of shared physical presence online. Although these challenges were significant, facilitators nevertheless emphasized that the benefits provided by the accessibility of online groups outweighed these challenges. Necessary adaptations for cultivating group cohesion online are outlined and include capitalizing on moments of humor and spontaneity, using group activities, encouraging information sharing between participants using the chat and screen-sharing features, and using objects from participants' environments to gain deeper insight into their subjective worlds.
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 | 0,017 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,012 | 0,011 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».