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Record W2902963819 · doi:10.1177/0093854818812516

Substance Use Disorder Typologies of Canadian Federally Sentenced Men: Relationships With Institutional Behavior and Postrelease Success

2018· article· en· W2902963819 on OpenAlexaffabout
Marguerite Ternes, Shanna Farrell MacDonald, Madelon Cheverie

Bibliographic record

VenueCriminal Justice and Behavior · 2018
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsMinistry of Community Safety and Correctional Services
Fundersnot available
KeywordsPolysubstance dependenceRecidivismPsychologySubstance abuseIntervention (counseling)PsychiatryPoison controlSubstance useHuman factors and ergonomicsSuicide preventionInjury preventionClinical psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

This study explored the presence of subgroups of incarcerated offenders using cluster analysis. Subgroups were created based on severity of criminogenic needs, including substance use, from a retrospective cohort of 5,275 Canadian male incarcerated offenders. Five groups emerged: (a) Primarily Drug Users With Stable Employment/Education, (b) Primarily Drug Users with High Needs, (c) Polysubstance Users With Positive Social Supports, (d) Polysubstance Users With Severe Need for Intervention, and (e) Drug Offenders With Good Reintegration Potential. Sociodemographic factors, criminal history, institutional behavior, and rates of recidivism were explored across subgroups. Drug Offenders With Good Reintegration Potential had the lowest rates of institutional charges and recidivism, while offenders in the Primarily Drug With High Needs and Polysubstance With Severe Need for Intervention groups had the highest rates. These findings highlight that classification of offenders is complex and nuanced. Knowing the pattern and severity of substance use and criminogenic needs aids offender management.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.996

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 studies0.0000.001
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 head0.077
GPT teacher head0.302
Teacher spread0.224 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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