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Record W4407964913 · doi:10.2196/59829

Formative Evaluation of Suicide Prevention Websites for Men: Qualitative Study with Men at Risk of Suicide and with Potential Gatekeepers

2025· article· en· W4407964913 on OpenAlexvenueno aff
Doreen Reifegerste, Anna Wagner, Lisa Huber, Manuel Fastuca

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupFormative assessmentSuicide preventionPsychologyPsychological interventionIntervention (counseling)Qualitative researchPoison controlMedical educationApplied psychologyMedicinePsychiatryPedagogyMedical emergencySociology

Abstract

fetched live from OpenAlex

BACKGROUND: The suicide rate among men exceeds that of women worldwide. One important measure in suicide prevention for men is digital communication interventions, as they enable easy and anonymous access to information resources. This is especially important for men who might not be reached by traditional, in-person prevention methods. Thus, as part of an interdisciplinary project on suicide prevention for men, two specific digital communication prevention measures were developed: (1) a website to inform men at risk about suicide prevention, and (2) a website to educate potential gatekeepers who are in contact with men at risk of suicide about appropriate life-saving measures. Both websites needed evaluation to explore how they are perceived by (1) men and by (2) potential gatekeepers of men at risk of suicide. This is crucial, as existing research lacks formative evaluation that informs the development of intervention communication materials. OBJECTIVE: This study aimed to analyze whether these websites were perceived as (1) comprehensible and engaging, (2) authentic and trustworthy, as well as (3) useful by (potential) users. Furthermore, we examined (4) additional ideas for effective communication about suicide prevention. METHODS: We conducted (1) individual videoconference interviews with 24 men to evaluate the website and (2) four focus groups with 8 gatekeepers in each group (32 participants) to evaluate the online education program. The focus group sample was equally distributed regarding gender and age. Recruitment was conducted together with a field research partner who posted adverts on Facebook and Instagram (Meta) to reach as many potential participants as possible in an efficient way. All participants were asked to evaluate the intervention materials using a fictitious scenario of a man experiencing a mental health crisis before the interviews or focus groups took place. RESULTS: The videos were perceived as (1) catchy, comprehensible, and empathetic, but too long for a short introduction. A balanced mix of emotional and informative content was considered appropriate and helpful. The health information provided was perceived as (2) serious and trustworthy due to citing scientific institutions and video material of men who had experienced suicidal ideation. (3) The intervention's applicability for men experiencing acute crisis was critiqued, but it was regarded as very useful for comprehensive information. (4) Further communication channels and addressing other male subgroups or gender identities were presented as possible extensions of the program. CONCLUSIONS: Effective suicide prevention research should address both the groups at risk and their support network. Digital communication interventions can provide low-threshold access. Videos with personalized examples are important to give men someone to identify with, which validates their emotional responses and supports their self-esteem, while videos with experts provide relevant and credible information.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.007
Scholarly communication0.0030.003
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.098
GPT teacher head0.504
Teacher spread0.406 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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