Substance use frequency, device-sharing, and used substances in Canadian prisons: results from Correctional Service Canada’s National Health Survey
Bibliographic record
Abstract
PURPOSE: Using data from Correctional Service Canada's 2022 National Health Survey, this study aims to estimate the frequency of substance use and related behaviours in federal Canadian prisons. DESIGN/METHODOLOGY/APPROACH: Participants (N = 413) were incarcerated persons who understood French or English, had been continuously incarcerated for six months before the study's launch, consented to participate and reported substance use within the previous six months while in federal prison. Participants completed a self-report questionnaire about institutional substance use. FINDINGS: Among people who smoked substances (78.2%), the largest proportion reported smoking every day and using marijuana. Just over one-half (51.0%) "never" shared (or did not know if they shared) smoking equipment with others. Among those who snorted substances (63.0%), the largest proportion reported snorting one to three days a week and using bupropion. Just under one-half (49.2%) "sometimes" or "always" shared their snorting device with others. Among those who injected substances (12.3%), the largest proportion reported injecting one to three days a week or less than one day a month and using methamphetamine. Nearly four-fifths (58.8%) shared their needle. ORIGINALITY/VALUE: This study provides updated estimates of substance use among people who are incarcerated in Canadian federal prisons. Furthermore, this study provides the frequency of substance use, the most used substance types and device sharing information across three different ingestion methods, which has not been done previously.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".