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Record W2128390078 · doi:10.1037/0022-006x.74.4.743

Substance use and community violence: A test of the relation at the daily level.

2006· article· en· W2128390078 on OpenAlexaff
Edward P. Mulvey, Candice L. Odgers, Jennifer L. Skeem, William Gardner, Carol A. Schubert, Charles W. Lidz

Bibliographic record

VenueJournal of Consulting and Clinical Psychology · 2006
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsSimon Fraser University
FundersNational Institute of Mental Health
KeywordsPsychologyClinical psychologyPoison controlInjury preventionSubstance abuseHuman factors and ergonomicsSuicide preventionPsychiatrySubstance useDrugMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Prior research has consistently demonstrated an association between substance use and involvement in violence among individuals with mental illness. Yet little is known about the temporal quality of this relationship, largely because longitudinal data required to address this issue are not readily available. This study examined the relationship between substance use (alcohol, marijuana, and other drug use) and violence at the daily level within a sample of mentally ill individuals at high risk for frequent involvement in violence (N = 132). Results support the serial nature of substance use and violence, with an increased likelihood of violence on days following the use of alcohol or multiple drugs, but not the inverse relationship. Implications for the utility of substance use as a risk marker for the assessment of future violence are discussed.

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)0.005
metaresearch head score (Gemma)0.022
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.161
GPT teacher head0.418
Teacher spread0.257 · 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

Citations108
Published2006
Admission routes1
Has abstractyes

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