How Competitive is the Canadian Diagnostic Radiology Residency Match? Application and Matching Trends from 1991-2014
Bibliographic record
Abstract
PURPOSE: The study sought to evaluate application trends in Canadian diagnostic radiology residency programs and to assess the relative competitiveness of radiology as a specialty. METHODS: The Canadian Residency Matching Service Reports from 1991-2014 for Canadian graduates were used to extract the total residency positions and radiology residency positions, number of applicants to all specialties and to radiology, number of first-choice radiology applicants, number of unmatched radiology positions, and number of positions and applicants to each specialty. Ratios were calculated: radiology positions to applicants and first-choice applicants, first-choice radiology applicants to applicants for all specialties, and training positions to applicants in each specialty. Data trends and correlation coefficients were analysed. RESULTS: The number of radiology residency positions offered increased, with strong positive correlation (r = 0.91, P < .001), while the number of applicants increased with only a moderate positive correlation (r = 0.49, P = .03). Radiology was the most competitive in 1997, with a ratio of 0.32 positions/applicant. There was an increase of positions/applicant over time (decreasing competitiveness; r = 0.76, P < .001) but no change in positions/first-choice applicant (r = 0.11, P = .65). The highest percentage of applicants who ranked radiology as their first choice was in 2003 at 6.5% with a decrease in this percentage over time (r = -0.36, P = .13). Radiology is moderately competitive for positions/overall applicants and very competitive for positions/first-choice applicants compared to other specialties. CONCLUSIONS: The number of radiology residency positions has increased while the number of applicants has not grown commensurately. The match was most competitive in 1997, and decreased in subsequent years. Possible reasons include job market, reimbursement, and work environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".