Geriatric Core Competencies for Family Medicine Curriculum and Enhanced Skills: Care of Elderly
Bibliographic record
Abstract
BACKGROUND: There is a growing mandate for Family Medicine residency programs to directly assess residents' clinical competence in Care of the Elderly (COE). The objectives of this paper are to describe the development and implementation of incremental core competencies for Postgraduate Year (PGY)-I Integrated Geriatrics Family Medicine, PGY-II Geriatrics Rotation Family Medicine, and PGY-III Enhanced Skills COE for COE Diploma residents at a Canadian University. METHODS: Iterative expert panel process for the development of the core competencies, with a pre-defined process for implementation of the core competencies. RESULTS: Eighty-five core competencies were selected overall by the Working Group, with 57 core competencies selected for the PGY-I/II Family Medicine residents and an additional 28 selected for the PGY-III COE residents. The core competencies follow the CanMEDS Family Medicine roles. Both sets of core competencies are based on consensus. CONCLUSIONS: Due to demographic changes, it is essential that Family Physicians have the required skills and knowledge to care for the frail elderly. The core competencies described were developed for PGY-I/II Family Medicine residents and PGY-III Enhanced Skills COE, with a focus on the development of geriatric expertise for those patients that would most benefit.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".