3060 Canadian Geriatrics Society: the ageing care 5Ms competencies for graduating medical students
Bibliographic record
Abstract
Abstract Background/purpose To prepare future physicians to care for a growing ageing population, the Canadian Geriatrics Society (CGS) Education Committee formed a working group in 2019 to update the 2009 Core Competencies in the Care of Older Persons for Canadian Medical Students. The goal is to assist medical educators with developing relevant undergraduate medical curriculum. Methods The working group chose 5Msmodel and canMEDs framework to develop the competencies. A modified Delphi process was used. National participants were recruited and three rounds of Delphi surveys were conducted via survey monkey. A 7-point Likert scale was used for each competency statement. Results The first round was conducted in October 2019, n = 72, identifying the importance and skill level of the components of the competencies under three headings; knowledge, skills and attitudes. The second round was conducted in September 2020, n = 54, with proposed competencies under seven headings; ageing, caring for older adults, (5Ms: mind, mobility, medications, multi-complexity and matters the most with >70% agreement for all. Based on the strength of the agreement and comments, minor revisions were made and the final survey was conducted in June 2021. The agreement level for competencies varied from 85–98%. Thirty-three core geriatric competencies were developed under 7 headings. The CGS education committee approved the competencies in Dec 2021. Conclusion The 2021 Ageing Care 5 M Competencies framework integrates new concepts and knowledge that inform current practice in the field of geriatrics. Thirty-three core geriatric competencies for the graduating undergraduate medical student were developed and classified under 7 headings. The framework was distributed to the accreditation and examination bodies and Canadian medical schools and was published in Academic medicine. 2024 Feb 1;99(2):198–207. doi: 10.1097/ACM.0000000000005475. Epub 2023 Nov 19. Currently we are working on implementation of the competencies.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".