A retrospective analysis of specialty match rate and gender trends in Canadian residency applications (2019–2024)
Bibliographic record
Abstract
BACKGROUND: This study examines Canadian medical graduate (CMG) match outcomes from 2019 to 2024, focusing on applicant numbers, gender distribution, and match success. METHODS: A retrospective analysis was conducted using publicly available data from the CaRMS match reports. Specialty-specific application numbers and first-choice match rates were extracted. Match rates were calculated as the number of matriculates divided by the number of applicants, while competitiveness was determined by the number of first-choice applications per available position. Specialties were categorized into clinical, surgical, and diagnostic disciplines for trend analysis. RESULTS: From 2019 to 2024, CMG applicants decreased slightly from 5380 to 5346, while the total quota increased from 2800 to 2918. Family medicine saw a significant decrease in applications (r² = -0.849, p = 0.03), while anesthesiology had a significant increase (r² = 0.950, p < 0.01). Diagnostic disciplines like neuropathology decreased significantly (r² = -0.887, p = 0.02), while diagnostic radiology increased (r² = 0.842, p = 0.03). Surgical disciplines, including plastic surgery, had steady increases, with vascular surgery doubling its applications by 2023. Female applicants increased in clinical and surgical specialties but decreased in diagnostics. Match rates improved in family medicine (r2 = 0.964, p < 0.01), medical genetics (r2 = 0.817, p = 0.04), psychiatry (r2 = 0.839, p = 0.04), public health (r2 = 0.939, p < 0.01), and diagnostic and clinical pathology (r2 = 0.850, p = 0.03), while diagnostic radiology (r2 = -0.825, p = 0.04) declined. Female applicants had higher match rates in ophthalmology and pediatric neurology, while males led in orthopedic surgery. CONCLUSION: Shifts in Canadian residency match trends from 2019 to 2024 may reflect the impact of the COVID-19 pandemic and evolving societal priorities. Ongoing monitoring of these trends is essential to ensure alignment with healthcare needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".