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Record W3111410232 · doi:10.1177/1049732320975747

How to Save a Life: Vital Clues From Men Who Have Attempted Suicide

2020· article· en· W3111410232 on OpenAlexafffundabout
John L. Oliffe, Olivier Ferlatte, John S. Ogrodniczuk, Zac E. Seidler, David Kealy, Simon Rice

Bibliographic record

VenueQualitative Health Research · 2020
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversity of British Columbia
FundersNational Health and Medical Research CouncilMovember CanadaMichael Smith Health Research BC
KeywordsPsychologyIsolation (microbiology)Suicide preventionMental healthDistressThematic analysisQualitative researchMental distressPoison controlPsychiatryMedicinePsychotherapistSociologyMedical emergency

Abstract

fetched live from OpenAlex

Male suicide rates are high and rising, and important insights can be gleaned from understanding the experiences of men who have attempted suicide. Drawing from a grounded theory photovoice study of diverse Canadian men, three intertwined thematic processes were derived: (a) preceding death struggles, (b) life-ending attempts and saving graces, and (c) managing to stay alive post suicide attempt. Preceding death struggles were characterized by cumulative injuries, intensifying internalized pain, isolation, and participant's efforts for belongingness in diminishing their distress. Men's life-ending attempts included overdosing and jumping from bridges; independent of method, men's saving graces emerged as changing their minds or being saved by others. Managing to stay alive post suicide attempt relied on men's acceptance that their mental illness was unending but amenable to effective self-management with professional mental health care. The findings offer vital clues about how male suicide might be prevented.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.617
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.536
GPT teacher head0.587
Teacher spread0.052 · 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 teacher head, not a consensus.

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

Citations18
Published2020
Admission routes3
Has abstractyes

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