MétaCan
Menu
Back to cohort
Record W2060679383 · doi:10.1177/0093854812448786

Problem Gambling and Mental Health Comorbidity in Canadian Federal Offenders

2012· article· en· W2060679383 on OpenAlexaffabout
Denise L. Preston, Steven McAvoy, C. Scott Saunders, Laura Gillam, Aqeel Saied, Nigel E. Turner

Bibliographic record

VenueCriminal Justice and Behavior · 2012
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsPsychiatryPsychologyComorbidityMental healthAnxietyPopulationClinical psychologyDepression (economics)Substance abusePoison controlSuicide preventionIntervention (counseling)MedicineMedical emergencyEnvironmental health

Abstract

fetched live from OpenAlex

This article examines the relationship between problem gambling, mental health, and criminal behavior in a sample of incarcerated Canadian male federal offenders ( N = 254). In particular, the study compared correlates of problem gambling in the offender population with the correlates of problem gambling in a nonoffender population from a previous study. The offenders were assessed using self-report tests, interviews, and a file review. Of these offenders, 106 were interviewed in more depth. Results indicated that problem gambling was significantly correlated with social anxiety, depression, substance abuse, impulsiveness, and current and childhood attention-deficit/hyperactivity disorder (ADHD) symptoms. In addition, the results indicated that the correlates of problem gambling were similar in offender and nonoffender populations. The relationship of gambling problems to depression, anxiety, substance abuse, ADHD, and impulsiveness suggests that any intervention for this population needs to be comprehensive and take into consideration a broad range of clinical needs.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.149
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.180
GPT teacher head0.424
Teacher spread0.244 · 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

Citations16
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueCriminal Justice and BehaviorSame topicGambling Behavior and TreatmentsFrench-language works237,207