Transition rounds: A peer-led curriculum for internal medicine residents moving into senior roles
Bibliographic record
Abstract
Introduction: The transition from junior to senior resident in internal medicine marks an increase in responsibilities, including leading teams, supervising trainees, and triaging patients on call. Unlike the structured transition programs available for new interns, no formal curriculum exists to support residents moving into senior roles. At the university-affiliated teaching hospital described in his report, internal medicine residents assume the senior resident role during their postgraduate year 2; however, the timing and structure of this transition may vary across Canadian residency programs. This report outlines the implementation of a structured educational intervention, Transition Rounds, to better prepare junior residents for the challenges of becoming senior residents. Methods: A longitudinal Transition Rounds curriculum was piloted at a university-affiliated teaching hospital in Toronto, Canada, from March to June 2024. The chief medical resident facilitated weekly 60-minute drop-in sessions for junior residents on the Clinical Teaching Unit. Topics were based on identified gaps in junior resident preparedness, including triaging patients, supervising junior trainees, and improving efficiency. Sessions incorporated real clinical scenarios, with senior residents providing peer mentorship. Results: Attendance ranged from 4 to 10 residents per session. Case-based discussions and real-time feedback helped junior residents refine clinical decision-making and leadership skills. Informal participant feedback indicated reduced anxiety and increased confidence in managing senior-level responsibilities. The peer-led model proved feasible, leveraging existing resources without additional faculty burden. Discussion: Transition Rounds provided a practical, low-resource approach to addressing an educational gap in residency training.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".