Barriers and Facilitators to User Engagement and Moderation for Web-Based Peer Support Among Young People: Qualitative Study Using the Behavior Change Wheel Framework
Bibliographic record
Abstract
BACKGROUND: Peer support groups or web-based chats for young people offer anonymous peer support in judgment-free spaces, where users may share their thoughts and feelings with others who may have experienced similar situations. User engagement is crucial for effective web-based peer support; however, levels of engagement vary. While moderation of peer support groups can have a positive impact on the engagement of young people, effective moderation can be challenging to implement. OBJECTIVE: This study aimed to identify barriers and facilitators to user engagement with, and moderation of, web-based peer support groups among young people aged 16 to 25 years and to provide recommendations for enhancing this service. METHODS: Drawing upon the Theoretical Domains Framework (TDF) and the Behavior Change Wheel (BCW), this study conducted qualitative interviews and gathered open-ended questionnaires from service users and moderators of The Mix, the United Kingdom's leading web-based mental health platform providing peer support groups for young people. Semistructured interviews were conducted with 2 service users and 8 moderators, and open-ended questionnaires were completed by 7 service users. Themes were coded using the Capability, Opportunity, Motivation, and Behavior (COM-B) model and the TDF. The BCW tools were then used to identify relevant behavior change techniques to improve user engagement in, and moderation of, the service. RESULTS: Thematic analysis revealed a total of 20 inductive themes within 10 TDF domains-9 (45%) for engagement and 11 (55%) for moderation. Of these 20 themes, 3 (15%) were facilitators of engagement, 7 (35%) were facilitators of moderation, 4 (20%) were barriers to moderation, and 6 (30%) barriers to engagement. Results suggest that skills, knowledge, beliefs about consequences, intentions, emotions, and the social and physical environment are important factors influencing service users and moderators of group chats. In particular, supporting the improvement of memory, attention, and decision-making skills of those involved; adapting the physical environment to facilitate effective interactions; and reducing negative emotions are suggested to optimize the value and effectiveness of peer support groups for young people's mental health for both the service users and moderators of these services. CONCLUSIONS: The study demonstrates the effectiveness of the BCW approach and the use of the TDF and COM-B model to understand the influences on behavior in a systematic manner, especially for mental health and well-being interventions. The findings can be applied to design structured interventions to change behaviors related to the engagement with, and moderation of, web-based peer support groups and, in turn, improve mental health outcomes for young people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".