Academic detailing to improve appropriate opioid prescribing: a mixed-methods process evaluation
Bibliographic record
Abstract
BACKGROUND: Academic detailing, an educational outreach service for family physicians, was funded by the Ontario government to address gaps in opioid prescribing and pain management. We sought to evaluate the impact of academic detailing on opioid prescribing, and to understand how and why academic detailing may have influenced opioid prescribing. METHODS: In this mixed-methods study, we collected quantitative and qualitative data concurrently from 2017 to 2019 in Ontario, Canada. We analyzed prescribing outcomes descriptively for a sample of participating physicians and compared them with a matched control group. We invited physicians to participate in qualitative interviews to discuss their experiences in academic detailing. Development and analysis of qualitative interviews was informed by the Theoretical Domains Framework. We triangulated qualitative and quantitative findings to understand the mechanisms that drove changes in opioid prescribing. RESULTS: = 238). Seventeen physicians completed interviews and reported that academic detailing addressed barriers to pain care, including lack of confidence, difficult interactions with patients and prescribing and tapering decisions. Academic detailing reinforced knowledge about opioid prescribing and pain management. Discussion of complex patients and talking points to use during challenging conversations were described as key drivers of practice change. INTERPRETATION: The findings of this real-world, mixed-methods evaluation explain how an academic detailing service addressed key barriers and enablers to limit high-risk opioid prescribing in primary care. This nuanced understanding will be used to inform, spread and scale academic detailing.
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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 | 0.174 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".