Oncology Education for Canadian Internal Medicine Residents: The Value of Participating in a Medical Oncology Elective Rotation
Bibliographic record
Abstract
Background: Despite the high incidence and burden of cancer in Canadians, medical oncology (MO) rotations are not mandatory in most Canadian internal medicine (IM) residency training programs. Methods: All IM residents scheduled for a MO rotation at 4 Canadian teaching cancer centres between 1 January 2013 and 31 December 2015 were invited to complete an online survey before and after their rotation. The survey was designed to evaluate perceptions of oncology, comfort in managing cancer patients, and basic oncology knowledge. Results: The survey was completed by 68 IM residents pre-rotation and by 48 (71%) post-rotation. Cancer-related learning was acquired mostly from MO physicians in clinic (35%). Self-directed learning, didactic teaching, and resident or fellow teaching accounted for 31%, 26%, and 10% respectively of learning acquisition. Comfort level in dealing with cancer patients and patients at end of life improved to 4.0/5 from 3.2/5 (p < 0.001) and to 4.0/5 from 3.6/5 (p = 0.003) respectively. Mean knowledge assessment score improved to 83% post-rotation from 76% pre-rotation (p = 0.003), with the greatest increase observed in general knowledge of common malignancies. The 3 topics ranked as most important to learn during a MO rotation were oncologic emergencies, common complications of treatment, and approach to diagnosis of cancer. Conclusions: A rotation in MO improves the perceptions of IM residents about oncology and their comfort level in dealing with cancer patients and patients at end of life. Overall cancer knowledge is also improved. Given those benefits, IM residency programs should encourage most of their residents to complete a MO rotation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".