Bibliographic record
Abstract
A cohort study that looked at Canadian province British Columbia's Safer Opioid Supply policy, instituted two years ago, found that there was a moderate increase in the number of individuals with at least one opioid prescription, a large increase in the number of opioid prescriptions dispensed, and a substantial increase in hospitalizations related to opioid poisoning. According to the study, “British Columbia's Safer Opioid Supply Policy and Opioid Outcomes” published online in JAMA Internal Medicine on Jan. 16, there were no statistically significant changes in deaths from opioid overdoses and no significant change in the number of prescribers. Rather, those prescribers who were there before prescribed a significantly greater amount of opioids. And the opioid‐related poisoning hospitalization rate increased by 3.2 per 100,000 population. The study, by Hai V. Nguyen, Ph.D. and colleagues, was funded by the Canadian Institutes of Health Research. The study used quarterly data from 2016 to 2022 from British Columbia, where the Safer Opioid Supply policy was implemented, then compared that data to Canada's Manitoba and Saskatchewan provinces where the policy was not implemented. The main outcomes were rates of prescriptions, claimants, and prescribers of opioids targeted by the Safer Opioid Supply policy (hydromorphone, morphine, oxycodone, and fentanyl); opioid‐related poisoning hospitalizations; and deaths from apparent opioid toxicity. The researchers concluded that while the Safer Opioid Supply policy was associated with higher rates of safer (prescription) supply, it was also associated with a significant increase in opioid‐related poisoning hospitalizations. “These findings will help inform ongoing debates about this policy not only in British Columbia, but also in other jurisdictions that are contemplating it,” the researchers concluded.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".