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Record W2092996567 · doi:10.1177/0093854811421596

Applying Risk/Need Assessment to Probation Practice and its Impact on the Recidivism of Young Offenders

2011· article· en· W2092996567 on OpenAlexaffabout
Duyen Luong, J. Stephen Wormith

Bibliographic record

VenueCriminal Justice and Behavior · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRecidivismRisk assessmentPsychologyPsychological interventionSample (material)Risk managementApplied psychologyClinical psychologyPsychiatryActuarial scienceComputer securityBusinessComputer science

Abstract

fetched live from OpenAlex

Evaluating the extent to which case management practices are guided by risk/need assessment is important because the impact of the assessment process will not be realized if the instrument is not applied as fully intended. This study investigated whether risk/need assessment is linked to the case management of young offenders and whether adherence to the principles of risk, need, and responsivity, as part of the case management plan, is related to recidivism. Data were collected on a sample of 192 young offenders. The Level of Service Inventory–Saskatchewan Youth Edition (LSI-SK) total score and seven of the eight subscale scores were positively correlated with recidivism. Generally, the LSI-SK was used to inform supervision intensity and interventions toward criminogenic needs. Moreover, adherence to the need principle was associated with reductions in recidivism. Implications for case management and direction for future research are discussed.

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)0.005
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.106
GPT teacher head0.399
Teacher spread0.293 · 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 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

Citations128
Published2011
Admission routes2
Has abstractyes

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