Updated Inventory and Projected Requirements for Specialist Physicians in Geriatrics
Bibliographic record
Abstract
BACKGROUND: The predicted growth of Canadians aged 65+ and the resultant rise in the demand for specialized geriatric services (SGS) requires physician resource planning. We updated the 2011 Canadian Geriatrics Society physician resource inventory and created projections for 2025 and 2030. METHODS: The number and full-time equivalents (FTEs) of geriatricians and Care of the Elderly (COE) physicians working in SGS were determined. FTE counts for 2025 and 2030 were estimated by accounting for retirements and trainees. A ratio of 1.25/10,000 population 65+ was used to predict physician resource requirements. RESULTS: Between 2011 and 2019 the number of geriatricians and COE physicians increased from 276 (235.8 FTEs) and 128 (89.9 FTEs), respectively, to 376 (319.6 FTEs) and 354 (115.5 FTEs). This increase did not keep pace with the 65+ population growth. The current gap between supply and need is expected to increase. DISCUSSION: The physician supply gap is projected to widen in 2025 and 2030. Increased recruitment and interdisciplinary team-based care, supported by enhanced funding models, and full integration of COE physicians in SGS could reduce this increasing gap. In contrast to pediatrician supply in Canada, the specialist physician resources available to the population 65+ reflect a disparity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".