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Record W2167223816 · doi:10.1177/0093854814558505

Trauma and Mental Health Problems in Adolescent Males

2014· article· en· W2167223816 on OpenAlexaff
Machteld Hoeve, Olivier F. Colins, E. Mulder, Rolf Loeber, Geert Jan J. M. Stams, Robert Vermeiren

Bibliographic record

VenueCriminal Justice and Behavior · 2014
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsWorkplace Health, Safety and Compensation Commission
FundersZonMwFP7 People: Marie-Curie ActionsEuropean Commission
KeywordsMental healthJuvenile delinquencyModerationPsychologyPsychiatrySuicide preventionPoison controlOccupational safety and healthInjury preventionClinical psychologyHuman factors and ergonomicsAdolescent healthMedicineMedical emergencySocial psychology

Abstract

fetched live from OpenAlex

Justice-involved youths are more likely to have mental health problems than peers in the community. Therefore, it is important to develop an understanding of the antecedents of mental health problems in this group. The present study examined the association between childhood trauma and mental health problems in juvenile justice-involved adolescent males ( N = 422), comparing childhood-onset with adolescent-onset offenders. Childhood-onset offenders were more likely than adolescent-onset offenders to report mental health and substance use problems, as well as childhood maltreatment. Via structural equation modeling, we found that childhood trauma predicted mental health problems in both offender groups. Multigroup analysis revealed a moderation effect of offender group: The association between trauma and mental health problems was stronger in adolescent-onset offenders than in childhood-onset offenders. Thus, mental health problems were more prevalent in childhood-onset offenders, but these problems were less well-explained by childhood trauma in childhood-onset than in adolescent-onset offenders. Theoretical and practical implications 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.476

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 head0.065
GPT teacher head0.348
Teacher spread0.283 · 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

Citations25
Published2014
Admission routes1
Has abstractyes

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