Development of a national PGME pain and substance use curriculum: Using an environmental scan to identify existing gaps
Bibliographic record
Abstract
Context: Opioid overdoses and surging death rates are a national public health crisis for Canada. Primary healthcare providers have a key role to play in curbing the opioid epidemic and this begins with education. In January 2021, the Association of the Faculties of Medicine of Canada (AFMC), launched an online pain management and opioid stewardship curriculum for integration into Canada’s 17 medical school undergraduate programs. To ground this initiative within the medical education continuum, the AFMC is extending this curriculum into postgraduate medical education (PGME). Objective: To conduct an environmental scan to identify gaps in current PGME educational resources related to pain management and substance/opioid use disorders. Study Design: Scoping review, document analysis, surveys, interviews. Setting and Population Studied: A scoping review of patient experiences with pain and opioid management. A document analyses of Canadian Pain Guidelines and Competency Frameworks, the Royal College of Physicians of Canada CanMEDS key and enabling competencies, the College of Family Physicians of Canada’s 105 Priority Topics, and PGME pain-related curricula from Canada’s 17 medical schools. Surveys to key stakeholders in PGME offices, and interviews with stakeholders from key partner associations. Outcome Measures/Results: Current PGME curricular offerings have been praised for their value, interactive nature, and use of up-to-date evidence. The challenges of existing curricula included a lack of formative assessments, lack of a developmental approach in PGME, a need to address issues of stigma and bias in opioid use and prescribing, and concerns about translating knowledge into clinical practice. Future educational programs should ensure that curricula focus on person-centered approaches to care that prioritize patients’ lived experiences, stigma and bias around opioid use and prescribing, evidence-based guidelines, and developing competencies based on the full-range of CanMEDS competencies and the College of Family Physicians of Canada skill dimensions. Conclusion: Through a collaborative approach, AFMC is currently working with experts in the field, and patient subject matter experts and family advocates to begin identifying the key topics and learning outcomes for a national PGME pain management and substance/opioid use disorder curriculum.
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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 | 0.034 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".