Provider Reactions to Opioid-Prescribing Report Cards
Bibliographic record
Abstract
OBJECTIVE: To evaluate prescribers' reactions and self-reported intentions to change prescribing behavior in response to opioid-prescribing report cards. DESIGN: We surveyed a sample of licensed prescribers in the state of Colorado registered with the state's prescription drug monitoring program (PDMP). SETTING: In 2018, Colorado disseminated tailored opioid-prescribing report cards to increase use of the PDMP and improve opioid prescribing. Report cards reflected individual prescribing history and compared individuals with an aggregate of others in the same specialty. Surveys were sent to approximately 29 000 prescribers registered with the PDMP 12 weeks after report card distribution. If respondents were not sent a report card, they were shown a sample report. Respondents were asked about their perceptions of the usefulness of the information and intentions to change their prescribing. PARTICIPANTS: A total of 3784 prescribers responded to the survey. MAIN OUTCOME MEASURES: Respondents were asked about their attitudes and reactions to an opioid-prescribing report card. Answers were given in the form of a 5-point Likert scale or multiple-choice questions. RESULTS: Of those who responded, 53.6% were male and nearly half (49.5%) had spent more than 20 years in practice. Among prescribers who recalled receiving a report card, most felt the reports were easy to understand (87.4%) and provided new information (82.8%). Two-thirds of prescribers who saw their reports felt the information accurately reflected their prescribing practices. Overall, 40.0% reported they planned to change their prescribing behaviors as a result of the information provided. The most useful metrics identified by prescribers were the number of patients with multiple providers and the number of patients receiving dangerous combination therapy. CONCLUSIONS: Overall, perceptions of the usefulness and accuracy of the report cards were positive. Understanding how the reports are perceived is a key factor to their use and influence. Further tailoring of the report to prescribers of different specialties and experience may enhance the effectiveness of the report cards.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".