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Record W7056596511

Gender differences in mental health needs and recidivism in a sample of adolescent offenders / Harpreet Kaur Chattha. --

2004· other· en· W7056596511 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2004
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsRecidivismMental healthSample (material)Exploratory researchPoison controlSuicide prevention
DOInot available

Abstract

fetched live from OpenAlex

The present study investigated gender differences in mental health needs and correlates of recidivism in a sample of court-referred youths in Thunder Bay, Ontario.Archival data, consisting of mental health assessments used to assist dispositional proceedings and recidivism data collected from 1996 to 2000, was examined in an exploratory fashion that was aided, in part, by prior empirical literature and relevant theoretical constructs.The analyses of historical information and behaviour checkists suggest that gender-specific mental health needs do exist in adolescents committing crimes.Female youths were reported as experiencing more internalizing and externalizing problems than the males.In addition, significantly more of the females were exposed to maltreatment, compared to the male youths.Although overall survival distributions of recidivism did not differ significantly by gender, there were differences in the risk factors for recidivism for male and female youths.It was found that poor mother-child relationship, poor parental management and substance abuse problems significantly influenced recidivism in males, while internalizing problems influenced female recidivism.While limitations of the current study are acknowledged, the findings, to some extent, reconcile some of the discrepancies and ambiguities in the literature.Important directions for future research are also 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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.042
GPT teacher head0.260
Teacher spread0.218 · 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.

Study designObservational
Domainnot available
GenreOther

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

Citations0
Published2004
Admission routes1
Has abstractyes

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