The Role of the Medical School Training on Physician Opioid Prescribing Practices: Evidence from Ontario, Canada: Le rôle de la formation à la faculté de médecine à l’égard des pratiques de prescription d’opioïdes des médecins: données probantes d’Ontario, Canada
Bibliographic record
Abstract
Background: Recent research found that physicians who completed medical school training at top-ranked U.S. medical schools prescribed fewer opioids than those trained at lower ranked schools, suggesting that physician training may play a role in the opioid epidemic. We replicated this analysis to understand whether this finding holds for Ontario, Canada. Methods: We used data on all opioid prescriptions written by Ontario physicians between 2013 and 2017 from the Narcotics Monitoring System. Using the Corporate Provider Database and ICES Physician Database, which contain medical school of training, we linked patients who filled opioid prescriptions with their respective prescribing physician. Available data on Canadian medical school rankings were obtained from Maclean’s news magazine. We used regression analysis to assess the relationship between number of opioid prescriptions and medical school ranking. Results: Compared to the United States, average annual number of opioid prescriptions per physician was lower in Ontario (236 vs. 78). Unlike the United States, we found little evidence that physicians trained at lower ranked medical schools prescribed more than their top-ranked school counterparts after controlling for specialty and location of practice. However, primary care physicians trained at non-English-speaking foreign schools prescribed the most opioids even after excluding opioid maintenance therapy–related prescriptions. Conclusion: The role of medical school training on opioid prescribing patterns among Ontario physicians differs from that in the United States likely due to greater homogeneity of curricula among Canadian schools. Ensuring physicians trained abroad receive additional pain management/addiction training may help address part of the opioid epidemic 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".