Canadian Otolaryngology - Head and Neck Surgery clerkship curricula: Evolving toward tomorrow's learners
Bibliographic record
Abstract
BACKGROUND: Increasing focus is being placed on Clerkship curriculum design and implementation in light of new undergraduate medical education research and accreditation standards. Canadian Otolaryngology-Head and Neck Surgery (OTOHNS) Clerkship programs are continually but independently evolving towards a common goal of improving Clerkship curriculum. METHODS: An electronic survey was sent to undergraduate OTOHNS directors at all Canadian medical schools (n = 17) examining their Clerkship curricula. Themes included Clerkship format, teaching methods, faculty support and development, program strengths, and barriers. RESULTS: Survey response rate was 76%. All responding schools had OTOHNS Clerkship programs ranging in type (mandatory, selective or elective) and length (<1 to 4 weeks). Learning modalities varied. Electronic learning tools were identified as increasingly important to curriculum delivery. Common strengths included wide clinical exposure and one-on-one mentoring. Multiple challenges were identified in curriculum implementation and evaluation. All schools expressed interest in developing national standards, objectives and e-learning resources. CONCLUSIONS: Significant variation exists in OTOHNS Clerkship experiences between Canadian medical schools. Many schools perceive barriers of insufficient time, space and curriculum standardization. Interested Canadian OTOHNS educators are eager to collaborate to improve the collective OTOHNS Clerkship experience.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".