Specialist Physicians in Geriatrics— Report of the Canadian Geriatrics Society Physician Resource Work Group
Bibliographic record
Abstract
BACKGROUND: At the 2011 Annual Business Meeting of the Canadian Geriatrics Society (CGS), an ad hoc Work Group was struck to submit a report providing an estimate of the number of physicians and full-time equivalents (FTEs) currently working in the field of geriatrics, an estimate of the number required (if possible), and a clearer understanding of what has to be done to move physician resource planning in geriatrics forward in Canada. METHODS: It was decided to focus on specialist physicians in geriatrics (defined as those who have completed advanced clinical training or have equivalent work experience in geriatrics and who limit a significant portion of their work-related activities to the duties of a consultant). RESULTS: In 2012, there are 230-242 certified specialists in geriatric medicine and approximately 326.15 FTE functional specialists in geriatrics. While this is less than the number required, no precise estimate of present and future need could be provided, as no attempts at a national physician resource plan in geriatrics based on utilization and demand forecasting, needs-based planning, and/or benchmarking have taken place. CONCLUSIONS: This would be an opportune time for the CGS to become more involved in physician resource planning. In addition to this being critical for the future health of our field of practice, there is increasing interest in aligning specialty training with societal needs (n = 216).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".