Securing an OTL-HNS residency: How competitive is it? Comparing medical student perceptions to actual Canadian statistics
Bibliographic record
Abstract
BACKGROUND: The residency match is an important event in an aspiring physician's career. Otolaryngology - Head and Neck Surgery (OTL-HNS) is a surgical specialty that has enjoyed high numbers of applicants to its residency programs. However, recent trends in Canada show a decline in first-choice applicants to several surgical fields. Factors thought to influence a medical student's choice include role models, career opportunities and work-life balance. The notion of perceived competitiveness is a factor that has not yet been explored. This study sought to compare competitiveness of OTL-HNS, as perceived by Canadian medical students to residency match statistics published yearly by CaRMS (Canadian Residency Matching Service), with the hope of informing future decisions of surgical residency programs. METHODS: An electronic survey was created and distributed to all medical students enrolled in the 17 Canadian medical schools. After gathering demographic information, students were asked to rank what they perceived to be the five most competitive disciplines offered by CaRMS. They were also asked to rank surgical specialties from most to least competitive. Publically available data from CaRMS was then collected and analyzed to determine actual competitiveness of admissions to Canadian OTL-HNS residency programs. RESULTS: 1194 students, from first to fourth year of medical school, completed the survey. CaRMS statistics over the period from 2008 to 2014 demonstrated that the five most competitive specialties were Plastic Surgery, Dermatology, Ophthalmology, Emergency Medicine and OTL-HNS. Among surgical disciplines, OTL-HNS was third most competitive, where on average 72% of students match to their first-choice discipline. When students were questioned, 35% ranked OTL-HNS amongst the top five most competitive. On the other hand 72%, 74% and 80% recognized Opthalmology, Dermatology and Plastic Surgery as being among the five most competitive, respectively. We found that fourth-year medical students were significantly more knowledgeable about the competitiveness of both OTL-HNS and Plastic Surgery compared to first-year students (p < 0.01). CONCLUSION: Overall, Canadian medical students may underestimate the competitiveness of OTL-HNS. Furthermore, competitiveness would appear to be a concept that resonates with medical students during the match process.
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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 | 0.011 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".