Needing Treatment: A Snapshot of Provincially Incarcerated Adult Offenders in Nova Scotia with a View towards Substance Abuse and Population Health
Bibliographic record
Abstract
Currently, Nova Scotia Correctional Services offers little by way of programming or treatment for adult offenders incarcerated under its authority, despite research showing a positive correlation between substance abuse and crime. Through both qualitative and quantitative data, this research report (1) situates the case of Nova Scotia within other literature that addresses crime and substance abuse; (2) presents a snapshot of the demographics and programming needs of provincially incarcerated adult offenders in Nova Scotia; (3) speaks to the need for accredited substance abuse programming for provincially incarcerated offenders; and (4) asserts that "warehousing" inmates may be addressed and potentially ameliorated through a population health approach to addiction policy, programming, and related treatment services. The purpose of this article is to report findings from research conducted with adult offenders incarcerated provincially across Nova Scotia, with a view to exploring links among crime, addiction, and population health. It is found that a large majority of provincial inmates in Nova Scotia are challenged by substance abuse, that crime in Nova Scotia, as elsewhere, is largely correlated to addiction, and that adult offenders appear to be motivated to participate in substance abuse programming while in custody. Prospects for future research are also considered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".