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Record W2782990578 · doi:10.1080/14789949.2018.1425474

Evaluating the utility of ‘strength’ items when assessing the risk of young offenders

2018· article· en· W2782990578 on OpenAlexaff
Stephane M. Shepherd, Susanne Strand, Jodi L. Viljoen, Michael Daffern

Bibliographic record

VenueJournal of Forensic Psychiatry and Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychologyCohortJuvenile delinquencyClinical psychologyPsychiatryDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

There is emerging recognition that positive or pro-social characteristics may lessen criminal propensity. There are now several adult and youth forensic instruments that include protective or strength components. Yet evidence supporting the protective capacities of these instruments with youth offending populations is still developing. This study aimed to identity the prevalence of strength items on the Youth Level of Service/Case Management Inventory tool, and their relationships with risk and re-offending for a cohort of 212 multi-cultural Australian juveniles in custody. The prevalence of strengths in the sample was low, and differed by cultural group. Young people who possessed a strength yielded lower instrument total and domain scores and were more likely to be afforded a lower level of risk compared to youth without a strength. Moreover, youth who possessed a strength were significantly more likely to desist from re-offending. This association remained after controlling for level of risk. Findings point to the importance of strengths when assessing a young person’s risk for re-offending.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.072
GPT teacher head0.409
Teacher spread0.336 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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