Determinants of First Practice Location among Canadian Geriatric Medicine Trainees and Recent Graduates: Findings of a Cross-sectional Survey in 2023
Bibliographic record
Abstract
Background: There is a projected and growing gap of geriatricians in Canada. Geriatricians play a crucial role in addressing the health needs of older adults. We aimed to understand the factors that influence the choice of first-practice location for new geriatricians in the context of an aging Canadian population. Methods: We distributed an online survey to geriatric medicine subspecialty residents and recently licensed geriatricians in Canada. The survey was developed through expert opinions, career planning sessions, and a literature review. There were nine survey sections: general characteristics, location determinants, practice determinants, colleague determinants, support and space, non-clinical opportunities, income models, lifestyle factors, and recruitment determinants. The data were analyzed descriptively. Results: A total of 61 respondents (51 English, 10 French) completed the survey. The respondents were new practicing geriatricians (37) and geriatric medicine residents (24). Most planned to practice in Ontario (26.2%) or Quebec (27.9%), and 75.4% were women. Flexibility in work-life balance (95.1%), collegiality (93.4%) and reasonable call schedules (93.4%) were the most important factors influencing first practice location. Income did not rank among the highest priorities for choosing the first practice location relative to other factors but was mentioned in open-ended responses to recruitment questions. Conclusions: This is the first survey identifying the determinants of first practice location in geriatric medicine in Canada. Work-life balance and collegial support are a priority for new geriatricians and may be the strongest incentives a program can provide. For recruitment, income incentives may be beneficial to building new specialized geriatric services. Future research will examine determinants of first practice location among Care of the Elderly clinicians.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".