Enhancing professionalism in post-graduate medical education: the initial implementation and evaluation of a longitudinal curriculum for geriatrics residents in Toronto, Canada
Bibliographic record
Abstract
Background: Professionalism is vital in medicine, yet gaps exist in its teaching within post-graduate medical education. Methods: We developed an eight-session curriculum on professionalism for geriatrics residents at the University of Toronto. Topics included personal-professional identity, physician well-being, communication, and leadership, incorporating a focus on self-reflection throughout. We evaluated the curriculum in two ways: (1) we captured immediate reactions using standard evaluations with Likert-scale questions on teaching effectiveness. (2) Graduated residents participated in semi-structured interviews to assess deeper reactions and longer-term impacts on professional identity and practice. Interview transcripts were rigorously analyzed using thematic. Results: The teaching effectiveness scores averaged from 80 written evaluations were excellent: 4.45/5 (89%). We interviewed 12 of 22 eligible graduates (55%). Thematic analysis demonstrated that the curriculum impacted residents in three key thematic areas: (1) it led participants to understand their role as physicians in a more comprehensive way, while staying consistent with their personal values and strengths. (2) Communication skills training in particular equipped learners with important skills needed to enact their professional standards. (3) Through developing a supportive community and practicing mindful self-reflection, participants reported improvements in their well-being. Conclusions: This paper demonstrates that professionalism can and should be integrated into the formal curriculum of post-graduate medical education. Key success factors in our study included a focus on fostering peer support and training in communication skills as a concrete method to actualize personal professional standards.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".