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Record W2891761818 · doi:10.1108/mhsi-06-2018-0021

Research watch: men’s social inclusion and suicide prevention

2018· article· en· W2891761818 on OpenAlexaboutno aff
Sue Holttum

Bibliographic record

VenueMental Health and Social Inclusion · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHopefulnessMasculinitySuicide preventionValue (mathematics)PsychologyInclusion (mineral)OriginalitySocial supportQualitative researchMental healthPoison controlSocial psychologyMedicinePsychiatrySociologyMedical emergencySocial science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to explore recent research on reducing suicide, especially in men, who are often seen as excluding themselves from needing support, or they are excluded because people think they do not want it. Design/methodology/approach A search was carried out for recent papers on suicide prevention in men. Findings One study of 75 regions of Europe reported a link between higher value on giving social support and lower suicide rates, especially for men. Another study reported on the fall in a previously high suicide rate, especially in men, in Quebec province in Canada. A programme of suicide prevention may have contributed to this reduction. Finally, a small interview study reported on how certain kinds of encounters with professionals can inspire hope to carry on after a suicide attempt. Originality/value The two papers looking at regions (across Europe and one province of Canada) suggest how social forces may contribute to reducing suicide, especially in men. The Canadian study suggests the possibility that suicide might be reduced partly by enabling help-seeking in men to be seen as a positive aspect of masculine identity, rather than seeing masculinity as excluding men from support. The small qualitative study illustrates vividly how individual encounters after a suicide attempt might promote hopefulness and are relevant to both sexes.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0160.000
Scholarly communication0.0000.000
Open science0.0000.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.115
GPT teacher head0.481
Teacher spread0.366 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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