An innovative service collaboration to reduce criminal recidivism for inmates with severe addictions
Bibliographic record
Abstract
Agencies with overlapping mandates can form partnerships to aid development of effective programming. In 2008, the Dedicated Substance Abuse Treatment Unit (DSATU) opened at Regina Correctional Centre through a tripartite collaboration between the Saskatchewan (SK) Ministry of Justice, Corrections and Policing; Addiction Services, Regina Qu’Appelle Health Region; and the SK Ministry of Health. Stakeholders researched existing best practices in the field and developed an evidence-based substance abuse treatment program for sentenced inmates at high risk to re-offend. An evaluation of the DSATU program completed in 2016 concluded that the DSATU was effective, sustainable, and likely transferable to other correctional facilities wishing to offer this type of programming. The stakeholder partnership was a key ingredient in the program’s success. This paper describes the process by which the partners worked together to develop, implement, and sustain this innovative and evidence-based substance abuse treatment program. The partners’ willingness to compromise, to take a collaborative approach to building the partnership and developing the program, and to put clients ahead of individual organizational mandates all contributed to the success of the partnership.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".