Effects of a distance learning program on physicians' opioid- and benzodiazepine-prescribing skills
Bibliographic record
Abstract
INTRODUCTION: Opioid misuse is common among patients with chronic nonmalignant pain. There is a pressing need for physicians to increase their confidence and competence in managing these patients. METHODS: A randomized controlled trial of family physicians (N = 88) attending 1 of 4 continuing medical education events helped to determine the effectiveness of e-mail case discussions in changing physician behavior. Before random assignment, participants completed a pretest and attended a 3-hour didactic session on prescribing opioids and benzodiazepines. The intervention group participated in 10 weeks of e-mail case discussions, with designated participants responding to questions on cases. An addictions physician facilitated the discussion. Several months after the e-mail discussion, participants took part in a mock telephone consultation; a blinded researcher posing as a medical colleague asked for advice about 2 cases involving opioid and benzodiazepine prescribing. Using a checklist, the researcher recorded the questions asked and advice given by the physician. RESULTS: On post-testing, both groups expressed greater optimism about treatment outcomes and were more likely to report using a treatment contract and providing advice about sleep hygiene. There were no significant differences between pretesting and post-testing between the groups on the survey. During the telephone consultation, the intervention group asked significantly more questions and offered more advice than the control group (odds ratio for question items, 1.27 [p = .03]; advice items, 1.33 [p = .01). DISCUSSION: Facilitated by electronic mail and a medical expert, case discussion is an effective means of improving physician performance. Telephone consultation holds promise as a method for evaluating physicians' assessment and management skills.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.011 | 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".