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Record W4318475795 · doi:10.3390/ijerph20032259

Understanding Protective Factors for Men at Risk of Suicide Using the CHIME Framework: The Primacy of Relational Connectedness

2023· article· en· W4318475795 on OpenAlexaff
Katherine Boydell, Alexandra Nicolopoulos, Diane Macdonald, Stephanie Habak, Helen Christensen

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2023
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsProtective factorSocial connectednessPsychologyEmpowermentNarrativeMental healthQualitative researchSuicide preventionPoison controlSocial psychologyMedicinePsychiatrySociologyPolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

Suicide is a global problem, ranking among the leading causes of death in many countries across the world. Most people who die by suicide are "under the radar", having never seen a mental health professional or been diagnosed with a mental illness. This article describes the protective factors for men experiencing suicidal thoughts, plans, and/or attempts who are "under the radar". Using in-depth qualitative interviews, we aimed to understand stakeholder perspectives on the protective factors that influence men's wellbeing. The pervasiveness of relational connectedness in men's narratives was identified as a central protective factor. Other key protective factors included meaningful activity, empowerment, and hope. These results have the potential to facilitate the development of focused community initiatives. More generally, the current research offers an example of a qualitative inquiry into men's wellbeing that focuses on strengths and positive factors in their lives and may provide a guide for future community-based suicide prevention research.

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)0.006
metaresearch head score (Gemma)0.008
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 score0.013
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0050.009
Scholarly communication0.0050.005
Open science0.0010.007
Research integrity0.0010.003
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 head0.391
GPT teacher head0.469
Teacher spread0.078 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public Health→Same topicSuicide and Self-Harm Studies→French-language works237,207→