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Record W2232270673 · doi:10.1007/s10597-015-9986-x

Stigma in Male Depression and Suicide: A Canadian Sex Comparison Study

2016· article· en· W2232270673 on OpenAlexafffundabout
John L. Oliffe, John S. Ogrodniczuk, Susan Gordon, Genevieve Creighton, Mary T. Kelly, Nick Black, Corey S. Mackenzie

Bibliographic record

VenueCommunity Mental Health Journal · 2016
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of ManitobaUniversity of British Columbia
FundersMovember CanadaMovember Foundation
KeywordsDepression (economics)Suicide preventionPsychiatryInjury preventionPsychologyPoison controlOccupational safety and healthClinical psychologyHuman factors and ergonomicsStigma (botany)MedicineMedical emergency

Abstract

fetched live from OpenAlex

Stigma in men's depression and suicide can restrict help-seeking, reduce treatment compliance and deter individuals from confiding in friends and family. In this article we report sex comparison findings from a national survey of English-speaking adult Canadians about stigmatized beliefs concerning male depression and suicide. Among respondents without direct experience of depression or suicide (n = 541) more than a third endorsed the view that men with depression are unpredictable. Overall, a greater proportion of males endorsed stigmatizing views about male depression compared to female respondents. A greater proportion of female respondents endorsed items indicating that men who suicide are disconnected, lost and lonely. Male and female respondents with direct personal experience of depression or suicide (n = 360) strongly endorsed stigmatizing attitudes toward themselves and a greater proportion of male respondents indicated that they would be embarrassed about seeking help for depression.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0050.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.121
GPT teacher head0.427
Teacher spread0.307 · 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 designObservational
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

Citations167
Published2016
Admission routes3
Has abstractyes

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