The race that never slows: Otolaryngology - Head and Neck Surgery residency applicant parameters over time
Bibliographic record
Abstract
Background: There has been an increasing number of Canadian medical graduates who have gone unmatched in the residency selection process. Medical students have been engaging in extracurricular activities outside the formal curriculum which may help to distinguish themselves from their peers in the selection process. To understand how competitiveness in residency selection shapes applicant demographic characteristics and behaviours, this study set out to explore the demographic characteristics and prevalence of reported extra-curricular activities by applicants to Canadian Otolaryngology - Head & Neck Surgery (OTL-HNS) residency across time. Methods: A retrospective, descriptive study reviewed specific sections of the curriculum vitae (CV) of applicants to OTL-HNS programs in Canada. These sections were self-reported, and included research productivity, involvement in volunteer and leadership activities, membership in associations, and honours or awards granted. Data was quantified and analyzed descriptively. Results: Between 2013 to 2017, a total of 267 applicants reported a median of 12.6 research publications, 9.6 volunteer activities, six leadership activities, six association memberships and 9.8 honours/awards. Applicants were younger over time, with proportions of applicants over 30 years old decreasing from 56% in 2013 to 9% in 2017. Conclusion: Applicants to Canadian OTL-HNS residency programs are reporting consistently high numbers of extracurricular activities and were of increasingly younger ages. Medical students are investing significant time and energy to pursue these activities which are above and beyond the formal curriculum, possibly contributing to decreased diversity in applicants for competitive residencies, increasing the likelihood of misrepresentation in residency applications, and likely contributing to medical student burnout.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.005 | 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".