The Impact of a New PGME Curriculum for Pain Management and Substance Use Disorder in Canada
Bibliographic record
Abstract
Context Residency education in pain management and substance use disorder in Canada is rarely delivered as a formal course. Often, it appears in fragments across multiple sessions, rotations, and specialty rotations. The lack of a standardized training and the challenge of the ongoing opioid crisis have led to the development and implementation of a new competency-based pain management and substance use disorder curriculum by the Association of Faculties of Medicine of Canada (AFMC). This study presents findings from a pilot evaluation study that examined the impact of the new curriculum on PGME residents’ knowledge of and ability to manage the various aspects of pain and substance use disorder. Setting/Data/Population We conducted pre-and post-evaluation surveys (n = 38) with PGME residents across multiple residencies in Canada. Descriptive and inferential statistical techniques (means, t-tests, Cohen’s D) were used to assess the impact of the training and the extent of improvement. Intervention PGME residents participated in an online training program consisting of six modules in pain management and substance use disorder between March and July 2022. Outcome Measures and Results Overall, participants demonstrated significant improvement in their perceived ability to identify, describe, explain, discuss, and manage all the learning outcomes assessed in the curriculum. Notably, participants’ ability to manage patients with substance use disorder increased by 45% (t = - 5.09, p < 0.05). Participants’ ability to apply planned, evidence-based, and responsive opioid tapering discussions increased by 57% (t = - 7.18, p < 0.05) while their ability to safely deprescribe opioids as safely as possible increased by 50% (t = - 6.35, p < 0.05). Conclusions The new curriculum impacted residents in this pilot significantly. Residents demonstrated significant improvements in the knowledge of opioids use and management. It has the potential to bridge the knowledge gap in pain management and substance use disorder in residency training across Canada. Upon completion of this curriculum, Canadian residents will be able to treat patients more effectively and will be better positioned to help tackle the opioid 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".