MétaCan
Menu
Back to cohort
Record W2800449426 · doi:10.1111/eip.12667

Externalizing depression symptoms among Canadian males with recent suicidal ideation: A focus on young men

2018· article· en· W2800449426 on OpenAlexafffundabout
Simon Rice, David Kealy, John L. Oliffe, John S. Ogrodniczuk

Bibliographic record

VenueEarly Intervention in Psychiatry · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of British Columbia
FundersMovember Canada
KeywordsSuicidal ideationDepression (economics)AngerPsychiatryPsychologyClinical psychologyDistressSuicide preventionPoison controlInjury preventionYoung adultMedicineDevelopmental psychologyMedical emergency

Abstract

fetched live from OpenAlex

AIM: The primary aim was to quantify, relative to older men, young men's externalizing of depression symptoms and past-month suicidal ideation. METHODS: A non-probability national sample of 1000 Canadian men self-reported internalizing and externalizing symptoms of depression and past-month suicidal ideation. Stratification quotas reflected Canadian census data to age and region. RESULTS: Young men (18-25 years) were at markedly higher risk of past-month suicidal ideation than were older men. When controlling for internalizing depression, a multivariate age × recent suicidal ideation interaction indicated higher externalizing of depression symptoms in young men relative to older men, especially for those reporting recent suicidal ideation (P < .001). Interactions were observed for drug use, anger and aggression, and risk-taking domains. A sizable proportion of younger men were uniquely identified by the MDRS-22. CONCLUSIONS: Screening tools that include assessment of externalizing symptoms may assist in improving detection of distress and suicide risk in young men.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.301
Teacher spread0.285 · 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

Citations29
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueEarly Intervention in PsychiatrySame topicSuicide and Self-Harm StudiesFrench-language works237,207