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Record W4391544599 · doi:10.17583/ijep.12741

Neuropsychological Assessment in Schooled Adolescent Offenders and Non-Offenders

2024· article· en· W4391544599 on OpenAlexaboutno aff
Ronald Ruiz Peña, Mariana Pino, Juan Contreras

Bibliographic record

VenueInternational Journal of Educational Psychology · 2024
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
FundersUniversidad del Magdalena
KeywordsNeuropsychologyPsychologyNeuropsychological assessmentClinical psychologyJuvenile delinquencyPsychiatryCognition

Abstract

fetched live from OpenAlex

Adolescents who break the law have experienced situations that increase the likelihood of becoming involved in criminal activities such as drug use, gang involvement, adverse economic conditions, among others. All this, added to their stage of human development, which is characterized by physical, cognitive, social and emotional changes, can lead them to have deficiencies in their cognitive processes and at the same time present educational difficulties. The purpose of this study is to evaluate different cognitive processes of these adolescents in comparison to a control group with similar characteristics but who have not committed any crime and whose education has not been interrupted. For this purpose, were included (n = 62) adolescent offenders and (n = 62) adolescent non-offenders of male sex and aged 14 to 18 years was taken. Basic sociodemographic data on their education and psychoactive substance use were collected, as well as cognitive data with tests such as Ineco Frontal Screening for executive functions, Montreal Cognitive Assessment for general functions, among others. The results showed significant differences in executive functions, attentional processes, memory and language. These difficulties can be key to school performance, therefore, educational interventions adapted to these adolescents are suggested.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.463
Teacher spread0.407 · 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
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
Published2024
Admission routes1
Has abstractyes

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