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Record W2057628000 · doi:10.1080/14999013.2015.1014528

Prevalence and Correlates of Criminal Activity in Adolescents Treated in Adult Inpatient Mental Health Beds in Ontario, Canada

2015· article· en· W2057628000 on OpenAlexaffabout
Shannon L. Stewart, Philip Baiden, Wendy den Dunnen, John P. Hirdes, Christopher M. Perlman

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2015
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of OttawaUniversity of WaterlooUniversity of TorontoWestern University
Fundersnot available
KeywordsMental healthPsychiatryCriminal justiceQuarter (Canadian coin)MedicineLogistic regressionSubstance abuseAddictionMental illnessClinical psychologyPsychologyCriminology

Abstract

fetched live from OpenAlex

Using logistic regression, this study seeks to examine the prevalence and correlates of criminal involvement in the previous year among adolescents in inpatient psychiatric facilities across Ontario, Canada. A sample of 2,613 adolescents aged 12 to 18 years who were admitted to adult inpatient mental health beds were examined. Just over one quarter of adolescents engaged in criminal activity within the past year. Older age, male gender, previous psychiatric admissions, a history of child abuse, poor insight into mental illness, substance use, specific types of mental health disorders, and aggressive behavior were all significantly associated with the presence of prior criminal activity. The well-founded association between mental health problems, substance use, and criminal behavior highlights the need for effective screening in settings providing services in the areas of juvenile justice, mental health, and addictions. Clinician awareness in all three settings is recommended so that these factors associated with at-risk behavior can be identified and appropriate treatment and referrals can be provided at the earliest point of involvement with any of these service systems.

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 score0.146
Threshold uncertainty score0.790

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 head0.025
GPT teacher head0.306
Teacher spread0.281 · 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 routes2
Has abstractyes

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