Developing a National UGME Pain Management and Substance Use Disorder Curriculum to Address the Opioid Crisis: A Program Evaluation Pilot Study
Bibliographic record
Abstract
Abstract Background Pain is one of the most common reasons for adults to seek health care, yet educational program focused on pain are often underrepresented in medical school curricula. In January 2021, the Association of Faculties of Medicine of Canada (AFMC) launched an online national, bilingual, competency-based curriculum for undergraduate medical (UGME) students in pain management and substance use in response to the opioid crisis to bridge the content gaps in programs across Canada. The purpose of this study is to evaluation the pilot of this national curriculum. Methods Undergraduate medical education students (n = 168) from across Canada participated in the program evaluation of a pilot which ran from September to November 2020. Participants completed online pre- and post-program surveys that assessed the influence of the curriculum on participants’ knowledge as well as the value, usability, and feasibility of this curriculum. Results Participants’ perceived confidence their new knowledge and in utilizing resources required to maintain their knowledge significantly increased (75% and 51% respectively). Their perceived knowledge that addressed the 72 learning objectives within the curriculum significantly increased from pre- to post-program. Over 90% of participants reported that the was valuable, feasible, and usable. The most frequently discussed program strengths were the clear and comprehensive content, interactive and well-organized design, and relevance of curriculum content for future clinical practice. The overall weakness of the curriculum included the length, repetition of content, the lack of clarity and relevance of the assessment questions, end-user technology issues, and French translation discrepancies. Participant’s recommendations for improving the curriculum included streamlining content, addressing technology issues, and enhancing the clarity and relevance of assessment questions embedded within each of the modules. Conclusion Participants agreed that an online pain management and substance use curriculum is a valuable, usable, and feasible learning opportunity. Given the severity of the opioid crisis in Canada, these online modules provide a curriculum that can be integrated into existing UGME programs or can provide self-directed learning.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".