Prescription opioid use and misuse: piloting an educational strategy for rural primary care physicians.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the feasibility and effectiveness of a multifaceted educational intervention to improve the opioid prescribing practices of rural family physicians in a remote First Nations community. DESIGN: Prospective cohort study. SETTING: Sioux Lookout, Ont. PARTICIPANTS: Family physicians. INTERVENTIONS: Eighteen family physicians participated in a 1-year study of a series of educational interventions on safe opioid prescribing. Interventions included a main workshop with a lecture and interactive case discussions, an online chat room, video case conferencing, and consultant support. MAIN OUTCOME MEASURES: Responses to questionnaires at baseline and after 1 year on knowledge, attitudes, and practices related to opioid prescribing. RESULTS: The main workshop was feasible and was well received by primary care physicians in remote communities. At 1 year, physicians were less concerned about getting patients addicted to opioids and more comfortable with opioid dosing. CONCLUSION: Multifaceted education and consultant support might play an important role in improving family physician comfort with opioid prescribing, and could improve the treatment of chronic pain while minimizing the risk of addiction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".