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Record W1935789697 · doi:10.1111/acer.12788

Precipitating Circumstances of Suicide and Alcohol Intoxication Among U.S. Ethnic Groups

2015· article· en· W1935789697 on OpenAlexaff
Raúl Caetano, Mark S. Kaplan, Nathalie Huguet, Kenneth R. Conner, Bentson H. McFarland, Norman Giesbrecht, Kurt B. Nolte

Bibliographic record

VenueAlcoholism Clinical and Experimental Research · 2015
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsCentre for Addiction and Mental Health
FundersNational Institute on Alcohol Abuse and AlcoholismCenters for Disease Control and Prevention
KeywordsEthnic groupMedicineDemographyAlcoholSuicide preventionAlcohol intoxicationInjury preventionAlcohol abuseBlood alcoholOccupational safety and healthPoison controlSubstance abusePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Our goal was to assess the prevalence of 9 different types of precipitating circumstances among suicide decedents, and examine the association between circumstances and postmortem blood alcohol concentration (BAC ≥ 0.08 g/dl) across U.S. ethnic groups. METHODS: Data come from the restricted 2003 to 2011 National Violent Death Reporting System, with postmortem information on 59,384 male and female suicide decedents for 17 U.S. states. RESULTS: Among men, precipitating circumstances statistically associated with a BAC ≥ 0.08 g/dl were physical health and job problems for Blacks, and experiencing a crisis, physical health problems, and intimate partner problem for Hispanics. Among women, the only precipitating circumstance associated with a BAC ≥ 0.08 g/dl was substance abuse problems other than alcohol for Blacks. The number of precipitating circumstances present before the suicide was negatively associated with a BAC ≥ 0.08 g/dl for Whites, Blacks, and Hispanics. CONCLUSIONS: Selected precipitating circumstances were associated with a BAC ≥ 0.08 g/dl, and the strongest determinant of this level of alcohol intoxication prior to suicide among all ethnic groups was the presence of an alcohol problem.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.432
GPT teacher head0.555
Teacher spread0.123 · 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.

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

Citations12
Published2015
Admission routes1
Has abstractyes

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