Health Needs for Suicide Prevention and Acceptance of e-Mental Health Interventions in Adolescents and Young Adults: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Adolescence is a phase of higher vulnerability for suicidal behavior. In Germany, almost 500 adolescents and young adults aged 15-25 years commit suicide each year. Youths in rural areas are characterized by a higher likelihood of poorer mental health. In rural areas, appropriate support for adolescents and young adults in mental health crises is difficult to access. The general acceptability of digital communication in youths can make the provision of an eHealth tool a promising strategy. OBJECTIVE: The aim of this study was to explore the health needs regarding suicide prevention for adolescents and young adults in rural areas of Germany and Switzerland and to identify characteristics of suitable e-mental health interventions. METHODS: This study reports on a qualitative secondary analysis of archived data, which had been collected through formative participatory research. Using 32 semistructured interviews (individually or in groups of 2) with 13 adolescents and young adults (aged 18-25 years) and 23 experts from relevant fields, we applied a deductive-inductive methodological approach and used qualitative content analyses according to Kuckartz (2016). RESULTS: Experts as well as adolescents and young adults have reported health needs in digital suicide prevention. The health needs for rural adolescents and young adults in crises were characterized by several categories. First, the need for suicide prevention in general was highlighted. Additionally, the need for a peer concept and web-based suicide prevention were stressed. The factors influencing the acceptability of a peer-driven, web-based support were related to low-threshold access, lifelike intervention, anonymity, and trustworthiness. CONCLUSIONS: The results suggest a need for suicide prevention services for adolescents and young adults in this rural setting. Peer-driven and web-based suicide prevention services may add an important element of support during crises. By establishing such a service, an improvement in mental health support and well-being could be enabled. These services should be developed with the participation of the target group, taking anonymity, trustworthiness, and low-threshold access into account.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".