How are we preparing doctors for their roles as patient educators? Exploring undergraduate and postgraduate curricula in Canadian medical schools
Bibliographic record
Abstract
Background: Although patient education (PE) has been identified as an important strategy to support patients with low health literacy, medical trainees report feeling ill-prepared for this responsibility. Our goal was to explore how PE training is incorporated centrally into undergraduate (UGME) and postgraduate (PGME) education across Canada, with the aim of proposing a PE curriculum. Methods: We circulated a web-based survey to all Canadian UGME and PGME Associate Deans, subsequently expanding the scope of our investigation by surveying Family Medicine and Pediatrics program directors. Data analysis involved a combination of frequency calculations and conventional qualitative content analysis. Results: According to survey respondents, PE was taught centrally in 72% of UGME curricula, 25% of PGME curricula, and 25% and 82% of Pediatrics and Family Medicine programs respectively. PE was predominantly incorporated into communication skills curricula, and role modeling was the most common teaching modality. Barriers included lack of time and low curricular priority; facilitators included embedding PE into communication skills training and use of patient partners and standardized patients. Conclusions: PE has not been uniformly implemented in a centralized manner across Canadian UGME and PGME curricula. Based on our survey data and the relevant literature, we propose a sample longitudinal curriculum spanning UGME and PGME and recommend that PE be explicitly framed as a communication skill.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".