Changing or validating physician opioid prescribing behaviors through audit and feedback and academic detailing interventions in primary care
Bibliographic record
Abstract
Background In Ontario, Canada, province-wide initiatives supporting safer opioid prescribing in primary care include voluntary audit and feedback reports and academic detailing. In this process evaluation, we aimed to determine the fidelity of delivery and receipt of the interventions, the observed change strategies used by physicians, potential mechanisms of action, and how complementary the initiatives can be to each other. Method Semi-structured interviews were conducted with academic detailers and with physicians who received both interventions. Academic detailer interviews were coded using the Behavior Change Technique Taxonomy; physician interviews were coded to the Theoretical Domain Framework. Change strategies were summarized based on academic detailer intentions and physician-reported practice changes. Potential mechanisms of action were identified using the Theories and Techniques Tool and the literature. Patient partners informed the interpretation of results through ongoing group discussions of preliminary findings. Results Interviews were conducted with eight academic detailers and 12 physicians. Change strategies described by academic detailers to support physicians’ opioid prescribing included problem solving, instructions on how to perform the behavior, adding objects to the environment, credible source, shaping knowledge, and social support. Physicians mentioned that academic detailing validated current opioid practices or increased their belief about capabilities and their intentions, mediated by increased skills and the impact of environmental context and resources. Potential mechanisms of action included behavioral regulation, behavioral cueing, and general attitudes/beliefs. On its own, receiving the audit and feedback report did not lead to changes in beliefs about prescribing practices; however, for some physicians, it provided validation and reassurance. Physicians saw unrealized potential for complementarity. Conclusions New interventions are often implemented in a complex ecosystem with other competing interventions. In this study, we show how examining the fidelity of the intervention from initial design through to delivery can identify opportunities for potential optimization.
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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 | 0.033 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".