Otolaryngology-Head and Neck Surgery clinical electives in undergraduate medicine: a cross-sectional observational study
Bibliographic record
Abstract
Abstract Background Otolaryngology-Head and Neck Surgery (OHNS) electives provide medical students opportunities for knowledge acquisition, mentorship, and career exploration. Given the importance of electives on medical student education, this study examines OHNS clinical electives prior to their cancellation in 2020 due to the COVID-19 pandemic. Methods An anonymous 29-question electronic survey was created using the program “Qualtrics.” Themes included elective structure and organization, elective clinical and non-clinical teaching, evaluation of students, and the influence of electives on the Canadian Residency Match (CaRMS). The survey was distributed through the Canadian Society of Otolaryngology e-newsletter and e-mailed to all OHNS undergraduate and postgraduate program directors across Canada. Results Forty-two responses were received. The vast majority of respondents felt that visiting electives were important and should return post-COVID-19 (97.6%). Most said they provide more in-depth or hands-on teaching (52.4% and 59.6%, respectively). However, there was great variability in the feedback, types of teaching and curriculum provided to elective students. It was estimated that 77% of current residents at the postgraduate program that responders were affiliated with participated in an elective at their program. Conclusions Prior to the cancellation of visiting electives in 2020 due to the COVID-19 pandemic, electives played an important role in OHNS undergraduate medical education and career planning for students wishing to pursue a career in OHNS. Electives also provide the opportunity for the evaluation of students by OHNS postgraduate programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".