Opioids, Benzodiazepines and Z-Drugs: Alberta Physicians' Attitudes and Opinions upon Receipt of their Personalized Prescribing Profile
Bibliographic record
Abstract
Opioid prescriptions have been monitored by the College of Physicians and Surgeons of Alberta (CPSA) since 1986, and benzodiazepine prescriptions since 2015. Recently the CPSA developed the “MD Snapshot-Prescribing Profile,” a feedback intervention consisting of a personalized report for physicians to see how many opioids and/or benzodiazepines they have prescribed to their patients. The aim of this study was to determine the attitudes and opinions of physicians in Alberta who received their prescribing profile from the CPSA in December 2016. Following mail-out of the prescribing profile, an online survey was emailed to recipients (n=8,213). The mixed survey asked five closed-ended questions, and an open-ended question asking for comments. Results from the closed-ended questions were compiled via Survey Monkey and responses to the open-ended question were analyzed using a qualitative content analysis method. Total survey response rate was 27% (n=2,148). More than half of physician-respondents indicated that they plan to make changes to their prescribing practice based on the prescribing profile and two-thirds of respondents found the information in the prescribing profile useful. Responses to the open-ended question were mixed. Physicians' attitudes and opinions regarding the receipt of their prescribing profile are diverse. Most recipients found benefit in their profile, and plan to use forthcoming versions as a useful instrument in their practices. Given the high rates of opioid/benzodiazepine prescriptions and related opioid epidemic, the MD Snapshot-Prescribing Profile is an innovative and important tool that can assist in improving physician prescribing practices.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".