Physician prescribing of opioid agonist treatments in provincial correctional facilities in Ontario, Canada: A survey
Bibliographic record
Abstract
BACKGROUND: Substance use and substance use disorders are common in people who experience detention or incarceration in Canada, and opioid agonist treatment (OAT) may reduce the harms associated with substance use disorders. We aimed to define current physician practice in provincial correctional facilities in Ontario with respect to prescribing OAT and to identify potential barriers and facilitators to prescribing OAT. METHODS: We invited all physicians practicing in the 26 provincial correctional facilities for adults in Ontario to participate in an online survey. RESULTS: Twenty-seven physicians participated, with representation from most correctional facilities in Ontario. Of participating physicians, 52% reported prescribing methadone and 48% reported prescribing buprenorphine/naloxone to patients in provincial correctional facilities. Nineteen percent of participants reported initiating methadone treatment and 11% reported initiating buprenorphine/naloxone for patients in custody. Participants identified multiple barriers to initiating OAT in provincial correctional facilities including concerns about medication diversion and safety, concerns about initiating treatment in patients who are not currently using opioids, lack of linkage with community-based providers and the Ministry of Community Safety and Correctional Services policy. Identified facilitators to initiating OAT were support from institutional health care staff and administrative staff, adequate resources for program delivery and access to linkage with community-based OAT providers. CONCLUSIONS: This study identifies opportunities to improve OAT programs and to improve access to OAT for persons in provincial correctional facilities in Ontario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".