Oncology education in Canadian undergraduate and postgraduate medical programs: A survey of educators and learners.
Bibliographic record
Abstract
e16558 Background: Deficiencies in undergraduate (UG) oncology education have been documented and there is a lack of data on the quality and quantity of oncology education in postgraduate (PG) family medicine (FM) and internal medicine (IM) training programs. Methods: A self-administered web-survey was created to obtain information regarding the current oncology curriculum at medical schools and PG FM and IM training programs. Survey requests were sent to educators (undergraduate medical education curriculum committee members (UMECCM), family medicine (FMPD) and internal medicine program directors (IMPD), oncologists) and learners (final year medical students (MS), family medicine (FMR) and internal medicine residents (IMR)) at all 17 of Canada’s medical schools. Results: 159 of 961 educators (19 UMECCM, 7 FMPD, 10 IMPD, 54 medical oncologists, 67 radiation oncologists and 2 hematologic oncologists) and 518 of 1966 learners (342 MS, 95 FMR and 81 IMR) completed the survey. Overall response rate was 23% (educators 17%, learners 26%). Responses were received from at least one educator or learner from all 17 medical schools. The amount of oncology education was thought to be inadequate in their respective programs by 58% UMECCM, 57% FMPD and 50% IMPD. 82% of oncologists believed that oncology education was inadequate in their UG and PG FM and IM programs. For learners, oncology education was thought to be inadequate in their respective programs by 67% MS, 86% FMR and 63% IMR. Of 10 different categories of medical illness all groups agreed that their trainees were least adequately prepared to manage cancer. A standard set of oncology objectives was thought to be useful for UG learners by 59% of respondents and 61% for PG learners. The 3 topics considered most important as core competencies in oncology for both UG and PG learners are: cancer diagnosis, breaking bad news and cancer screening. Conclusions: Oncology education at the UG medical and PG FM and IM levels are currently thought to be inadequate by a majority of educators and learners. Developing a standard set of oncology objectives focusing on topics believed to be most important by educators may improve the quality of oncology education for learners.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".