Rates of opioid agonist treatment prescribing in provincial prisons in Ontario, Canada, 2015–2018: a repeated cross-sectional analysis
Bibliographic record
Abstract
OBJECTIVE: To describe opioid agonist treatment prescribing rates in provincial prisons and compare with community prescribing rates. DESIGN: We used quarterly, cross-sectional data on the number and proportion of people prescribed opioid agonist treatment in prison populations. Trends were compared with Ontario surveillance data from prescribers, reported on a monthly basis. SETTING: Provincial prisons and general population in Ontario, Canada between 2015 and 2018. PARTICIPANTS: Adults incarcerated in provincial prisons and people ages 15 years and older in Ontario. MAIN OUTCOMES AND MEASURES: Opioid agonist treatment prescribing prevalence, defined as treatment with methadone or buprenorphine/naloxone. RESULTS: In prison, 6.9%-8.4% of people were prescribed methadone; 0.8% to 4.8% buprenorphine/naloxone; and 8.2% to 13.2% either treatment over the study period. Between 2015 and 2018, methadone prescribing prevalence did not substantially change in prisons or in the general population. The prevalence rate of buprenorphine/naloxone prescribing increased in prisons by 1.70 times per year (95% CI 1.47 to 1.96), which was significantly higher than the increase in community prescribing: 1.20 (95% CI 1.19 to 1.21). Buprenorphine/naloxone prescribing prevalence was significantly different across prisons. CONCLUSIONS: The increase in opioid agonist treatment prescribing between 2015 and 2018 in provincial prisons shows that efforts to scale up access to treatment in the context of the opioid overdose crisis have included people who experience incarceration in Ontario. Further work is needed to understand unmet need for treatment and treatment impacts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".