Applicant and Match Trends to Geriatric-Focused Postgraduate Medical Training in Canada: A Descriptive Analysis
Bibliographic record
Abstract
Physicians with postgraduate training in caring for older adults-geriatricians, geriatric psychiatrists, and Care of the Elderly family physicians (FM-COE)-have expertise in managing complex care needs. Deficits in the geriatric-focused physician workforce coupled with the aging demographic necessitate an increase in training and clinical positions. Descriptive analyses of data from established matching systems have not occurred to understand the preferences and outcomes of applicants to geriatric-focused postgraduate training. This study describes applicant and match trends for geriatric-focused postgraduate training in Canada. In this retrospective cohort study, data from the Canadian Resident Matching Service and FM-COE program directors were analysed to examine program quotas, applicants' preferences, and match outcomes by medical school and over time. Based on their first-choice specialty ranking, applicants to geriatric medicine and FM-COE signalled a preference to pursue these programs and tended to match successfully. The proportion of unfilled training positions has increased in recent years, and the number of applicants has not increased consistently over time. There is a disparity between applicants to geriatric-focused training and the health human resources to meet population-level needs. Garnering interest among medical trainees is essential to address access and equity gaps.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".