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Record W2810272747 · doi:10.1177/1049732318782432

Constructing and Expanding Suicide Narratives From Gay Men

2018· article· en· W2810272747 on OpenAlexaff
Travis Salway, Dionne Gesink

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNarrativePrideInvisibilityHuman sexualityGender studiesPsychologyCoping (psychology)Identity (music)Minority stressSocial psychologySociologyLesbianSexual minorityClinical psychologyAestheticsPolitical scienceArtLiterature

Abstract

fetched live from OpenAlex

In this study, we document life stories of gay men who attempted suicide as adults. Our goal is to expand the collection of narratives used to understand this persistent health inequity. We interviewed seven adult gay men, each of whom had attempted suicide two to four times, and identified five narratives. Pride narratives resist any connection between sexuality and suicide. Trauma-and-stress narratives enable coping through acknowledgment of sexual stigma as a fundamental trauma and cause of subsequent stress and suicidal thoughts. Memorial narratives prevent suicide by maintaining a strong sense of "permanent" identity. Outing narratives demand that the listener confronts the legacy of unjust practices of homosexual surveillance and "outing," which historically resulted in gay suicides. Finally, postgay narratives warn of the risk of suicide among older generations of gay men who feel erased from the goals of modern gay movements. Sexual identity concealment or invisibility featured prominently in all five narratives.

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)0.009
metaresearch head score (Gemma)0.001
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 score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.619
GPT teacher head0.690
Teacher spread0.071 · 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

Citations34
Published2018
Admission routes1
Has abstractyes

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