MétaCan
Menu
← Back to cohort
Record W4411066209 · doi:10.2196/58891

Understanding the Needs of Moderators in Online Mental Health Forums: Realist Synthesis and Recommendations for Support

2025· article· en· W4411066209 on OpenAlexvenueno aff
Heather Robinson, Millissa Booth, Lauren Fothergill, Claire Friedrich, Zoe Glossop, Jade Haines, Andrew Harding, Rose Johnston, Steven Jones, Karen Machin, Rachel Meacock, Kristi Nielson, Paul Marshall, Jo‐Anne Puddephatt, Tamara Rakić, Paul Rayson, Jo Rycroft‐Malone, Nick Shryane, Zoe Swithenbank, Sara Wise, Fiona Lobban

Bibliographic record

VenueJMIR Mental Health · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintMental healthPsychologyComputer scienceWorld Wide WebPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: There has been an increase in the use of online mental health forums to support mental health. These forums are often moderated by trained moderators to ensure a safe, therapeutic environment. While the moderator role is rewarding, it can also be challenging. There is a need to understand the impact of the role on moderators and how they can best be supported to maintain psychological well-being. OBJECTIVE: This study aimed to understand how, why, and in what contexts moderator well-being is affected by the moderator role and produce actionable recommendations for how moderators can best be supported to maintain workplace psychological well-being. METHODS: We conducted realist synthesis of (1) published and gray literature from 2019 to 2023, (2) stakeholder interviews with forum moderators and hosts, and (3) moderator training manuals developed by organizations that host online mental health forums. Self-determination theory was used as the theoretical basis for this synthesis. RESULTS: We developed 24 context-mechanism-outcome configurations from our realist analysis of 9 published papers, 18 interviews, and 5 training manuals. The findings highlight the specific ways in which moderator well-being can be supported through meeting the psychological needs for autonomy, competence, and relatedness. Forums that allow moderators to work in alignment with their personal motivations can increase moderator well-being. Forum organizations should support moderator competence through initial expectation setting, especially around moderator responsibility for user well-being, and ongoing support, such as meaningful supervision and peer support. Co-designed training, reflective practice, and experiential learning are all key to increasing moderator competence and satisfaction in the workplace. Working within a diverse team with access to innovative forum design can increase moderator psychological well-being. Organizational support for moderators' well-being through monitoring and encouraging self-care is vital to ensure moderators can effectively carry out their role. Making and supporting meaningful relationships in the forum can boost psychological well-being and the therapeutic value of the moderator role. Key challenges for moderators were dealing with conflicts between supporting open discussion and ensuring a safe community environment, sharing lived experiences in positive ways for both moderator and user, and supporting people within the limitations of an anonymous forum. CONCLUSIONS: This realist synthesis is the first to examine the impacts on well-being of being a moderator of an online mental health forum. Recommendations to support moderator psychological well-being are proposed, targeted at specific stakeholder groups to aid implementation. Organizational-level endorsement and facilitation of support are particularly important for the realization of recommendations and interventions to support moderator well-being.

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)0.194
metaresearch head score (Gemma)0.346
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.194
Threshold uncertainty score0.994

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1940.346
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0110.010
Science and technology studies0.0080.010
Scholarly communication0.0200.030
Open science0.0070.013
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0170.002

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 head0.121
GPT teacher head0.456
Teacher spread0.335 · 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.

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

Explore more

Same venueJMIR Mental Health→Same topicDigital Mental Health Interventions→French-language works237,207→