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Record W2801282793 · doi:10.1177/1049732318769600

Men on Losing a Male to Suicide: A Gender Analysis

2018· article· en· W2801282793 on OpenAlexafffundabout
John L. Oliffe, Alex Broom, Mary T. Kelly, Joan L. Bottorff, Genevieve Creighton, Olivier Ferlatte

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHomicide, Infanticide, and Child Abuse
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersMovember CanadaMovember Foundation
KeywordsSuicide preventionPoison controlPsychologyInjury preventionHuman factors and ergonomicsPhotovoiceSuicide attemptPsychiatryMeaning (existential)Occupational safety and healthMedicineClinical psychologyMedical emergencyPsychotherapist

Abstract

fetched live from OpenAlex

Although male suicide has received research attention, the gendered experiences of men bereaved by male suicide are poorly understood. Addressing this knowledge gap, we share findings drawn from a photovoice study of Canadian-based men who had lost a male friend, partner, or family member to suicide. Two categories depicting the men's overall account of the suicide were inductively derived: (a) unforeseen suicide and (b) rationalized suicide. The "unforeseen suicides" referred to deaths that occurred without warning wherein participants spoke to tensions between having no idea that the deceased was at risk while reflecting on what they might have done to prevent the suicide. In contrast, "rationalized suicides" detailed an array of preexisting risk factors including mental illness and/or substance overuse to discuss cause-effect scenarios. Commonalities in unforeseen and rationalized suicides are discussed in the overarching theme, "managing emotions" whereby participants distanced themselves, but also drew meaning from the suicide.

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)0.023
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.558
GPT teacher head0.647
Teacher spread0.090 · 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; both teacher heads agree on what is shown here.

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

Citations17
Published2018
Admission routes3
Has abstractyes

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