A multi-year retrospective quantitative implementation evaluation of Safer Opioid Prescribing, an opioid prescribing continuing education program for Canadian healthcare providers
Bibliographic record
Abstract
Abstract Background: Continuing health professions education is considered an important policy intervention for the opioid epidemic. Besides examining effectiveness or impact, it is important to also study health policy implementation to understand how an intervention was delivered within complex policy and practice environments. Implementation outcomes can be used to help interpret continuing health profession education effects and impacts, help answer questions of “how” and “why” programs work, and inform transferability.Methods: We conducted a retrospective quantitative implementation evaluation of the 2014–2017 cohort of Safer Opioid Prescribing, a Canadian continuing health professions education program consisting of three synchronous webinars and in-person workshop. To measure reach and dose, we examined participation and completion data. We used Ontario physician demographic data, including regulatory status with respect to narcotics to examine relevant trends. To measure fidelity and participant responsiveness, we analyzed participant-provided evaluations of bias, active learning and relevance to practice. We used descriptive statistics and measures of association for both continuous and categorical variables. We used logistic regression to determine predictors of workshop participation and analysis of covariance to examine variation in satisfaction across different-sized sessions.Results: Eighty four percent of participants were family physicians with representative reach to non-major urban physicians. Webinar completion rate was 86.2% with no differences in completion based on rurality, gender or status with the regulatory college. Participants who had regulatory involvement with respect to opioids were more likely to be male, have been in practice for longer and participate in the workshop. Participants reported no significant bias and highly rated both active learning and relevance to practice regardless of their cohort size.Conclusions: This evaluation demonstrates that Safer Opioid Prescribing was implemented as intended. Over a short period and without any external funding, the program reached more than 1% of the Ontario physician workforce. This suggests that Safer Opioid Prescribing is a good model for using virtual continuing health professions education to reach a critical mass of prescribers to drive population level opioid utilization changes. This study represents a methodological advance of adapting evaluation methods from health policy and complex interventions for continuing health professions education.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".