Transcending technology boundaries and maintaining sense of community in virtual mental health peer support: a qualitative study with service providers and users
Bibliographic record
Abstract
BACKGROUND: This qualitative study explores the experiences of peer support workers (PSWs) and service users (or peers) during transition from in-person to virtual mental health services. During and following the COVID-19 pandemic, the need for accessible and community-based mental health support has become increasingly important. This research aims to understand how technological factors act as bridges and boundaries to mental health peer support services. In addition, the study explores whether and how a sense of community can be built or maintained among PSWs and peers in a virtual space when connections are mediated by technology. This research fills a gap in the literature by incorporating the perspectives of service users and underscores the potential of virtual peer support beyond pandemic conditions. METHODS: Data collection was conducted from a community organization that offers mental health peer support services. Semi-structured interviews were conducted with 13 employees and 27 service users. Thematic analysis was employed to identify key themes and synthesize a comprehensive understanding. RESULTS: The findings highlight the mental health peer support needs that were met through virtual services, the manifestation of technology-based boundaries and the steps taken to remove some of these boundaries, and the strategies employed by the organization and its members to establish and maintain a sense of community in a virtual environment marked by physical distancing and technology-mediated interrelations. The findings also reveal the importance of providing hybrid services consisting of a mixture of in person and virtual mental health support to reach a broad spectrum of service users. CONCLUSIONS: The study contributes to the ongoing efforts to enhance community mental health services and support in the virtual realm. It shows the importance of virtual peer support in situations where in-person support is not accessible. A hybrid model combining virtual and in-person mental health support services is recommended for better accessibility to mental health support services. Moreover, the importance of organizational support and of equitable resource allocation to overcome service boundaries are discussed.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".