Improving Recruitment into Geriatric Medicine in Canada: Findings and Recommendations from the Geriatric Recruitment Issues Study
Bibliographic record
Abstract
As the number of Canadians aged 65 and older continues to increase, declining recruitment into geriatric medicine (GM) raises concerns about the future viability of this medical subspecialty. To develop effective strategies to attract more GM trainees into the field, it is necessary to understand how medical students, residents, GM trainees, and specialists make career choices. The Geriatric Recruitment Issues Study (GRIST) was designed to assess specific methods that could be used to improve recruitment into geriatrics in Canada. Between November 2002 and January 2003, 530 participants were invited to complete the GRIST survey (117 Canadian geriatricians, 12 GM trainees, 96 internal medicine residents, and 305 senior medical students). Two hundred fifty-three surveys (47.7%) were completed and returned (from 54 participating geriatricians, 9 GM trainees, 50 internal medicine residents, and 140 senior medical students). The survey asked respondents to rate factors influencing their choice of medical career, the attractiveness of GM, and the anticipated effectiveness of potential recruitment strategies. Although feedback varied across the four groups on these issues, consistencies were observed between medical students and residents and between GM trainees and geriatricians. All groups agreed that role modeling was effective and that summer student research programs were an ineffective recruitment strategy. Based on the GRIST findings, this article proposes six recommendations for improving recruitment into Canadian geriatric medicine training programs.
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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 | 0.086 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".