Application rates to surgical residency programs in Canada
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to identify if the previously reported declining interest in surgery amongst medical students persists, and also to provide more descriptive analysis of trends by surgical specialty and medical school. Our hypothesis is that the previously reported decreasing interest in surgery remains constant for some surgical disciplines. METHODS: The Canadian Resident Matching Service and the Association of Faculties of Medicine of Canada provided data for this study. Several metrics of interest in surgery, including overall application trends, applications by discipline, and rankings by school of graduation were evaluated. Descriptive statistics and linear regression modeling were used. RESULTS: Between 2007 and 2017 the number of non-surgical residency positions and Canadian medical graduates increased significantly. However, the number of surgical residency positions and applications to surgical programs did not change significantly. The number of rankings to orthopedic and vascular surgery decreased significantly. Likewise, applicants to general, orthopedic, plastic, otolaryngology, and vascular surgery decreased significantly. Vascular surgery saw a significant decrease in first choice rankings. Total rankings to surgical programs increased significantly at McGill, with no significant change at other Canadian institutions. CONCLUSIONS: The findings of this study suggest that while the number of applicants to surgical residency positions has been consistent, it is not keeping pace with the growing number of both CMGs and non-surgical residency positions. Furthermore, by using other measures of medical student interest in surgical specialties, such as the total number of rankings to a specialty through the residency matching process, the total number of applicants applying to a surgical discipline and the total number of first choice ranks that each surgical discipline received, we have demonstrated that there is a possible declining interest in some surgical discipline.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".