Young Adults’ Perceptions of 2 Publicly Available Digital Resources for Self-injury: Qualitative Study of a Peer Support App and Web-Based Factsheets
Bibliographic record
Abstract
BACKGROUND: Digital resources have the potential to bridge the gaps in mental health services for young people who self-injure. Most research on digital resources for this population has involved observational studies of content in web-based communities or formative studies focused on the design and early evaluation of new interventions. Far less research has sought to understand young people's experiences with publicly available digital resources or to identify specific components of these resources that are perceived to be of value in their recovery. OBJECTIVE: This study aimed to understand young people's experiences with 2 publicly available digital resources for self-injury-a peer support app and web-based factsheets-and to disentangle potential explanatory mechanisms associated with perceived benefits and harms. METHODS: Participants were 96 individuals (aged 16-25 years) with nonsuicidal self-injury behavior in the past month, who recently completed a pilot randomized controlled trial designed to assess the efficacy of a peer support app as compared with web-based factsheets to reduce self-injury behavior. The trial showed that participants using the peer support app reported less self-injury behavior relative to those receiving the web-based factsheets over 8 weeks. In this study, we used a conventional approach to content analysis of responses to 2 open-ended questions delivered at the end of the trial with the aims of exploring participants' overall experiences with these resources and identifying the qualities of these resources that were perceived to be beneficial to or harmful for participants' recovery. RESULTS: Overall, participants were more likely to report benefits than harms. Participants who used the peer support app reported more harms than those who received the web-based factsheets. In the open coding phase, clear benefits were also derived from repeated weekly surveys about self-injury. Key benefits across digital resources included enhanced self-knowledge, reduction in self-injury activity, increased outreach or informal conversations, improved attitudes toward therapy, improved mood, and feeling supported and less alone. Key challenges included worsened or unchanged self-injury activity, diminished mood, and increased barriers to outreach. The most prominent benefit derived from the web-based factsheets and weekly surveys was improved self-understanding. However, the way this manifested differed, with factsheets providing insight on why participants engage in self-injury and the function it serves them and surveys making the frequency and severity of participants' behaviors more apparent. The benefits perceived from using the peer support app were general improvements in mood and feeling less alone. CONCLUSIONS: Findings contribute a nuanced understanding of young people's experiences with these digital resources and have implications for the optimization of existing platforms and the design of novel resources to support individuals who self-injure.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".