Developing Suicide Prevention Tools in the Context of Digital Peer Support: Qualitative Analysis of a Workshop With Multidisciplinary Stakeholders
Bibliographic record
Abstract
BACKGROUND: Suicide is the fourth leading cause of death among young people aged 15-29 years worldwide and suicide rates are increasing. Suicide prevention strategies can be effective but young people face barriers to accessing them. Providing support digitally can facilitate access, but this can also pose risks if there is inappropriate or harmful content. Collaborative approaches are key for developing digital suicide prevention tools to ensure support is appropriate and helpful for young people. Tellmi (previously MeeToo) is a premoderated UK-based peer-support app where people aged 11-25 years can anonymously discuss issues ranging from worries to life challenges. It has procedures to support high-risk users, nevertheless, Tellmi is interested in improving the support they provide to users with more acute mental health needs, such as young people struggling with suicide and self-harm ideation. Further research into the best ways of providing such support for this population is necessary. OBJECTIVE: The aim of this study is to explore the key considerations for developing and delivering digital suicide prevention tools for young people aged 18-25 years from a multidisciplinary perspective, including the views of young people, practitioners, and academics. METHODS: A full-day, in-person workshop was conducted with mental health academics (n=3) and mental health practitioners (n=2) with expertise in suicide prevention, young people with lived experience of suicidal ideation (n=4), and a computer scientist (n=1) and technical staff from the Tellmi app (n=6). Tellmi technical staff presented 14 possible evidence-based adaptations for the app as a basis for the discussions. A range of methods were used to evaluate them, including questionnaires to rate the ideas, annotating printouts of the ideas with post-it notes, and group discussions. A reflexive thematic analysis was performed on the qualitative data to explore key considerations for designing digital suicide prevention tools in the context of peer support. RESULTS: Participants discussed the needs of both those receiving and providing support, noting several key considerations for developing and delivering digital support for high-risk young people. In total, four themes were developed: (1) the aims of the app must be clear and consistent, (2) there are unique considerations for supporting high-risk users: (subtheme) customization helps tailor support to high-risk users, (3) "progress" is a broad and multifaceted concept, and (4) considering the roles of those providing support: (subtheme) expertise required to support app users and (subtheme) mitigating the impact of the role on supporters. CONCLUSIONS: This study outlined suggestions that may be beneficial for developing digital suicide prevention tools for young people. Suggestions included apps being customizable, transparent, accessible, visually appealing, and working with users to develop content and language. Future research should further explore this with a diverse group of young people and clinicians.
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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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".