Meeting the needs of future physicians: a core curriculum initiative for postgraduate medical education at a Canadian university
Bibliographic record
Abstract
UNLABELLED: In addition to possessing medical expertise, contemporary physicians are expected to be skilled communicators, critical consumers and users of medical research, teachers, collaborators, health care advocates, and managers. A core curriculum is a common set of learning experiences designed to help prepare physicians for these complex roles. PURPOSE: This article describes the design and implementation of one core curriculum, summarizes the feedback received from residents, and shares some of the lessons we are learning as we use feedback to develop our programme. METHOD: The core curriculum described was implemented at a Canadian university which offers 56 residency programmes with a total enrollment of approximately 360 students. The curriculum consisted of 30 sessions organized around four themes: biostatistics and epidemiology; communications and teaching skills; healthcare management, and ethical, medicolegal and lifestyle issues. Each session in the Core Curriculum was evaluated by residents with respect to the timing, quality, and value of the learning experience. In addition, residents participated in focus group discussions of their Core Curriculum experiences. RESULTS: Key findings related to the characteristics of effective core curriculum learning experiences and to the barriers to implementing a core curriculum across programmes. Of particular salience were findings related to explicit issues of attendance and the diverse needs of learners and programmes, and to more implicit issues of communication and managing change. The specific content and format of the Core Curriculum and the results of the evaluation process will be of interest to others considering a core curriculum for postgraduate medical programmes.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".