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Record W2889852797 · doi:10.3138/jmvfh.2017-0037

Contagion versus commemoration: public responses to suicide within Canadian military and Veteran populations

2018· article· en· W2889852797 on OpenAlexafffundvenueabout
Matthew J. Barrett

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsQueen's University
FundersCanadian Psychological AssociationCanadian Armed ForcesU.S. Department of Veterans Affairs
KeywordsGovernment (linguistics)Public healthCriminologySuicide preventionPublic policyPolitical sciencePerceptionPublic relationsMilitary personnelEpidemiologySociologyPoison controlPublic administrationMedicinePsychologyLawEnvironmental health

Abstract

fetched live from OpenAlex

Building on research into historical, public, and institutional perceptions of suicide, this article assesses the role of commemoration and remembrance in government, medical, and military responses to suicide within the Canadian Armed Forces (CAF). It provides an overview of how stakeholders and policy-makers have balanced commemoration with concerns over triggering a contagion effect in certain at-risk populations. The prevailing cultural beliefs of institutions and the public shape how military casualties have been defined and to what extent these deaths have been formally honoured. This article aims to prompt further research in the fields of history, epidemiology, sociology, and psychology into public attitudes toward suicide. Future interdisciplinary studies will provide stakeholders with a nuanced understanding of how the different priorities of the public, government, and the media influence their respective responses to the issue of suicide in military and Veteran populations.

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)0.004
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.007
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.003
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 head0.165
GPT teacher head0.392
Teacher spread0.227 · 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

Citations0
Published2018
Admission routes4
Has abstractyes

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