Implementing Changes to a Residency Program Curriculum before Competency-Based Medical Education: A Survey of Canadian Medical Oncology Program Directors
Bibliographic record
Abstract
Background: Postgraduate medical education is undergoing a paradigm shift in many universities worldwide, transitioning from a time-based model to competency-based medical education (cbme). Residency programs might have to alter clinical rotations, educational curricula, assessment methods, and faculty involvement in preparation for cbme, a process not yet characterized in the literature. Methods: We surveyed Canadian medical oncology program directors on planned or newly implemented residency program changes in preparation for cbme. Results: Prior to implementing cbme, all program directors changed at least 1 clinical rotation, most commonly making hematology/oncology (74%) entirely outpatient and eliminating radiation oncology (64%). Introductory rotations were altered to focus on common tumour sites, and later rotations were changed to increase learner autonomy. Most program directors planned to enhance resident learning with electronic teaching modules (79%), new training experiences (71%), and academic half-day changes (50%). Most program directors (64%) planned to change assessment methods to be entirely based on entrustable professional activities. All programs had developed a competence committee to review learner progress, and most (86%) had integrated academic coaches. Conclusions: Transitioning to cbme led to major structural and curricular changes within medical oncology training programs. Identifying these commonly implemented changes could help other programs transition to cbme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".