Undergraduate otolaryngology education at the University of Toronto: a review using a curriculum mapping system.
Bibliographic record
Abstract
BACKGROUND/PURPOSE: The aim of Canadian medical school curricula is to provide educational experiences that satisfy the specific objectives set out by the Medical Council of Canada. However, for specialties such as otolaryngology, there is considerable variability in student exposure to didactic and clinical teaching across Canadian medical schools, making it unclear whether students receive sufficient teaching of core otolaryngology content and clinical skills. The goal of this review was to assess the exposure to otolaryngology instruction in the undergraduate medical curriculum at the University of Toronto. METHOD: Otolaryngology objectives were derived from objectives created by the Medical Council of Canada and the University of Toronto. The University of Toronto's recently developed Curriculum Mapping System (CMap) was used to perform a keyword search of otolaryngology objectives to establish when and to what extent essential topics were being taught. RESULTS: All (10 of 10) major topics and skills identified were covered in the undergraduate medical curriculum. Although no major gaps were identified, an uneven distribution of teaching time exists. The majority (> 90%) of otolaryngology education occurs during year 1 of clerkship. The amount of preclerkship education was extremely limited. DISCUSSION AND CONCLUSIONS: Essential otolaryngology topics and skills are taught within the University of Toronto curriculum. The CMap was an effective tool to assess the otolaryngology curriculum and was able to identify gaps in otolaryngology education during the preclerkship years of medical school. As a result, modifications to the undergraduate curriculum have been implemented to provide additional teaching during the preclerkship years.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.001 | 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".