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Record W4411724151 · doi:10.2196/75320

Understanding Safety in Online Mental Health Forums: Realist Evaluation

2025· article· en· W4411724151 on OpenAlexvenueno aff
Paul Marshall, Neil Caton, Zoe Glossop, Steven Jones, Rachel Meacock, Paul Rayson, Heather Robinson, Fiona Lobban

Bibliographic record

VenueJMIR Mental Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthContext (archaeology)Social mediaPsychologyService providerMedicineNursingPsychiatryService (business)

Abstract

fetched live from OpenAlex

Background: Online forums are used widely to facilitate mental health peer support. However, concerns exist regarding potential harm associated with their use, and little is known about forum safety from the user's perspective. Objective: This study sought to understand how users experience safety within online mental health peer support forums. Following previous research, safety was conceptualized with reference to both experiences of harm and feelings of interpersonal safety within the forum environment. Methods: Data were collected from 42 semistructured realist interviews and 504 cross-sectional survey responses from users of 3 UK-based online mental health forums. These included a forum hosted by a health service provider with subforums for anxiety, depression, and eating disorders; a corporate provider focused on young people's mental health; and a voluntary sector provider with subforums for general mental health support, eating disorders, and postpartum psychosis. A bespoke survey was used to obtain descriptive quantitative data regarding user perceptions of forum safety. Qualitative data were used to refine an initial program theories framework comprising context-mechanism-outcome configurations related to forum safety developed in previously published realist synthesis. Results: Survey responses revealed that over half of the participants felt safe to post because of online anonymity (n=202, 40.1% agreed and n=97, 19.2% strongly agreed), while a minority reported encountering distressing forum posts (n=95, 18.8% agreed and n=18, 3.6% strongly agreed) and expressed concern that talking about mental health online could make them feel worse (n=113, 22.4% agreed and n=17, 3.4% strongly agreed). Refined program theories highlight: (1) the disclosure-promoting effect of anonymity, related to the mitigation of concerns that users' mental health experiences could be linked to their offline identities; (2) the importance of proactive content moderation for addressing emerging safety issues; (3) a need for organizations to implement rule enforcement sensitively and balance between conversational openness and restricting topics likely to cause distress; (4) forum users' experiences of self-moderating their exposure to potentially distressing online content; and (5) how the perceived nonjudgement, authenticity, and similarity of other forum users generates interpersonal safety. Conclusions: This is the first realist evaluation to directly assess processes underpinning safety in online mental health forums. A key novel finding of this study is that safety emerges not only from harm reduction procedures but also from a facilitative interpersonal atmosphere defined by sensitive moderation and the sharing of lived experiences. Hosts should therefore remain attentive to both potential risks and opportunities to foster connections between community members.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.066
metaresearch head score (Gemma)0.147
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.147
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.426
GPT teacher head0.537
Teacher spread0.112 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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