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Record W41680877

Pyschopathy in Childhood and Adolescence: Implications for the Assessment and Management of Multi-Problem Youths

2002· article· en· W41680877 on OpenAlexaff
Gina M. Vincent, Stephen D. Hart

Bibliographic record

VenueThe Journal of the American Medical Association (JAMA) Network (American Medical Association) · 2002
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychologyDevelopmental psychology
DOInot available

Abstract

fetched live from OpenAlex

This book examines the research and theoretical bases for the creation of a risk-needs management instrument for violent adolescents and young adults. The proposed instrument includes risk indicators beginning pre-natally, pari-natally, at-birth, then through infancy, early childhood, middle childhood and, finally, adolescence. The main purpose of the instrument is to assist case managers responsible for providing positive interventions to families and children, at all childhood and adolecent life stages, in order to reduce the likelihood of violent behaviors. The case intervention strategy is based on the assumption that the earlier resources are provided, the more effective they will be. The data instrument will be structured so that the risk information is gathered cumulatively across age domains and can be used to match specific interventions with particular needs profiles of a family and child, adolescent or young adult. This book is of interest to researchers, policy-makers, and government and non-government agency workers who are involved with policies, programs and instruments focused on the prevention of youth and young adult violence. It can be used as an advanced text book in upper level undergraduate courses and graduate courses in psychology, criminology, social work and educational counseling which deal with the child and youth violence, especially its causes and preventive interventions.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.290
Teacher spread0.279 · 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

Citations47
Published2002
Admission routes1
Has abstractyes

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