Ontario Geriatric Specialist Physician Resources 2018
Bibliographic record
Abstract
BACKGROUND: The number of older adults with complex health needs in Ontario is growing. The Ministry of Health and Long-Term Care requested a resource mapping project to assess the current 2018 and projected 2025 number of specialist physician resources. METHODS: Geriatric specialist physicians were defined as geriatricians, geriatric psychiatrists, and Care of the Elderly (COE) physicians. We determined the current number of geriatricians, geriatric psychiatrists, and COEs and clinical full-time-equivalent complement (CFTE) for geriatric medicine and geriatric psychiatry specialists. We projected the number of new trainees expected to enter practice and the number of physicians expected to retire by 2025. We compared these numbers and projections against established specialist/population ratios for geriatricians and geriatric psychiatrists. RESULTS: There was a deficit of geriatricians and geriatric psychiatrists (geriatricians: CFTE deficit of 150.5; geriatric psychiatrists: CFTE deficit of 116.3). In 2025, the projected CFTE deficit of geriatricians will increase to at least 210.35 and geriatric psychiatrists to 194.6. Only about 30% of COE physicians work in direct support of specialized services for the elderly. CONCLUSIONS: There is significant current and anticipated undersupply in the required number of geriatricians, geriatric psychiatrists, and COE physicians to meet anticipated population demand.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".