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Record W2623603957 · doi:10.5430/wje.v7n3p1

Mental Health and the Juvenile Justice System: Issues Related to Treatment and Rehabilitation

2017· article· en· W2623603957 on OpenAlexvenueno aff
Katrina A. Hovey, Staci M. Zolkoski, Lyndal M. Bullock

Bibliographic record

VenueWorld Journal of Education · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthEconomic JusticeRehabilitationPsychologyEthnic groupJuvenileJuvenile delinquencyClinical psychologyDevelopmental psychologyApplied psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Children and youth with mental health issues and learning difficulties are common in the juvenile justice system andfinding ways to effectively rehabilitate, treat, and educate them is complicated, yet imperative. In this article, weexamine the prevalence rates of mental health disorders in youth involved in the juvenile justice system, discuss themyriad challenges involved youth face, present differences related to gender and race/ethnicity as well as provideinformation associated with how best to assist these youths. Additionally, significant influences such as cultural,behavioral, and educational issues related to detained youth will be presented. Developing a better understanding ofthe challenges faced by detainees as well as recognizing barriers to treatment and rehabilitation are key. Further,identifying effective support systems for rehabilitation and transition are addressed.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0070.003
Open science0.0010.006
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0070.000

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 head0.016
GPT teacher head0.354
Teacher spread0.338 · 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 designQualitative
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

Citations22
Published2017
Admission routes1
Has abstractyes

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