Substance Use Disorder Typologies of Canadian Federally Sentenced Men: Relationships With Institutional Behavior and Postrelease Success
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".