Development and pilot evaluation of an educational session to support sparing opioid prescriptions to opioid naïve patients in a Canadian primary care setting
Bibliographic record
Abstract
BACKGROUND: Prescribing rates of some analgesics decreased during the public health crisis. Yet, up to a quarter of opioid-naïve persons prescribed opioids for noncancer pain develop prescription opioid use disorder. We, therefore, sought to evaluate a pilot educational session to support primary care-based sparing of opioid analgesics for noncancer pain among opioid-naïve patients in British Columbia (BC). METHODS: Therapeutics Initiative in BC has launched an audit and feedback intervention. Individual prescribing portraits were mailed to opioid prescribers, followed by academic detailing webinars. The webinars' learning outcomes included defining the terms opioid naïve and opioid sparing, and educating attendees on the (lack of) evidence for opioid analgesics to treat noncancer pain. The primary outcome was change in knowledge measured by four multiple-choice questions at the outset and conclusion of the webinar. RESULTS: Two hundred participants attended four webinars; 124 (62%) responded to the knowledge questions. Community-based primary care professionals (80/65%) from mostly urban settings (77/62%) self-identified as family physicians (46/37%), residents (22/18%), nurse practitioners (24/19%), and others (32/26%). Twelve participants (10%) recalled receiving the individualized portraits. While the correct identification of opioid naïve definitions increased by 23%, the correct identification of opioid sparing declined by 7%. Knowledge of the gaps in high-quality evidence supporting opioid analgesics and risk tools increased by 26% and 35%, respectively. CONCLUSION: The educational session outlined in this pilot yielded mixed results but appeared acceptable to learners and may need further refinement to become a feasible way to train professionals to help tackle the current toxic drugs crisis.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".