Group Cohesion and Necessary Adaptations in Online Hearing Voices Peer Support Groups: Qualitative Study With Group Facilitators
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".