Developing Suicide Prevention Tools in the Context of Digital Peer Support: Qualitative Analysis of a Workshop With Multidisciplinary Stakeholders
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,010 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».