MétaCan
Menu
Back to cohort

Mental Health in the Attention Models for Juvenile Offenders. The Cases of Colombia, Argentina, United States and Canada

2018· article· en· W2898324648 on OpenAlexaboutno aff
Angelica Tórres Quintero, Juliana Villanueva, Maria Camila Jaramillo Bernal, Esteban Sotomayor Carreño, Catherine Gutiérrez Congote

Bibliographic record

VenueUniversitas Médica · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsJuvenile delinquencyPunitive damagesCriminologyMental healthCriminal justicePsychological interventionJuvenilePopulationEconomic JusticePolitical sciencePunishment (psychology)Latin AmericansIntervention (counseling)PsychologyIndigenousPsychiatryMedicineSocial psychologyEnvironmental healthLaw

Abstract

fetched live from OpenAlex

Abstract
 Objective: To investigate how mental health is understood and approached in the attention models of detention centers for the convicted underage population in Argentina, Colombia, United States and Canada. Methodology: A literature search was conducted using the following key words: adolescence, mental health, juvenile justice, juvenile delinquency, risk factors, and interventions. Searches were done through the search engine Pubmed. Additionally, public institution websites for each country were consulted. Conclusions: Juvenile delinquency is now understood as a multi-factorial phenomenon with multiple areas of intervention within which economic, domestic and social factors are considered relevant, since these favor the development of criminal behavior. A similarity was found between Colombian and Argentinian systems; both are based on restorative justice that seeks reparation and not punishment; which is why there are no punitive measures. When comparing Canada and the United States, it can be seen that Canada is more similar to Latin-American countries than to the United States, given that the latter uses punitive measures focused on the offender.
 

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.610

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 studies0.0010.000
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 head0.028
GPT teacher head0.277
Teacher spread0.249 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueUniversitas MédicaSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207