Post-CaRMS match survey for fourth year medical students
Bibliographic record
Abstract
Background: We aimed to analyze which medical school experiences contribute to success in an increasingly competitive CaRMS match. Methods: We surveyed all matched University of Saskatchewan 2019 medical graduates on characteristics of their applications: number of program applications, interviews obtained, experiences (research, volunteer, leadership), awards and money spent on the residency match process, and qualitative reflections on the process. Using published CaRMS statistics based on number of positions versus applicants, specialties were divided into high availability/low demand (HA) (e.g. family and internal medicine) and low availability/high demand (LA) (e.g. dermatology and emergency medicine). Quantitative results were analyzed using descriptive statistics, chi-square and t-tests, and qualitative results thematically. Results: Data from 27 of 94 matched students were included. LA applicants were more likely to report at least one research project on their CV (66.67% among LA vs. 15.38% among HA, n = 27, χ2 = 8.640, p = 0.013), with a greater number of research presentations (mean=3.75 presentations vs. 2.07, t (25) = -2.251, p = 0.033). LA applicants had more elective weeks outside Saskatchewan (mean 11.75 weeks vs. 7.40 weeks, t (25) = -2.532, p = 0.018). Other application variables were not different between groups. Some students endorsed broader electives strategies, others (especially in surgical disciplines) supported narrower ones. Students reported travel, financial burden, document submission, and uncertainty as the greatest match process stressors. Conclusions: LA applicants cited more research projects and presentations, spent more elective weeks outside Saskatchewan, but were otherwise similar to HA applicants. Further studies should be done on student factors in the residency 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".