The Aging Care 5Ms Competencies: A Modified Delphi Study to Revise Medical Student Competencies for the Care of Older Adults
Bibliographic record
Abstract
PURPOSE: To revise the 2009 Canadian Geriatrics Society (CGS) Core Competencies in the Care of Older Persons for Canadian Medical Students by applying current frameworks and using a modified Delphi process. METHOD: The working group chose the Geriatric 5Ms model and CanMEDS framework to develop and structure the competencies. National (i.e., Canadian) Delphi participants were recruited, and 3 Delphi survey rounds were conducted from 2019 to 2021. Each survey round collected quantitative data using a 7-point Likert scale (LS) and qualitative data using free-text comments. The purpose of the first round was to establish the importance of the components of the proposed competencies (categorized into 13 subsections) and identify additional themes. The second round assessed agreement with 31 proposed competencies organized into 7 themes: aging, caring for older adults, mind, mobility, medications, multicomplexity, and matters the most. The third survey-rated agreement levels after further revisions to the competencies were applied. The final 33 competencies were shared with survey participants for feedback and other stakeholders for external validation. RESULTS: Mean LSs for the importance of the 13 competency component subsections on the first survey varied from 5.11 to 6.54, with an agreement level of 73%-93%. New themes emerged from the qualitative comments. Mean LSs for the 31 competencies on the second survey ranged from 5.57 to 6.81, with an agreement level of 80%-97%. Mean LSs for the revised competencies on the third survey ranged from 5.83 to 6.65, with an agreement level of 83%-95%. CONCLUSIONS: The authors developed the 33 Aging Care 5Ms Competencies for Canadian medical students using a consensus process. The competencies fulfill an important need in medical education, and ultimately, society. The authors strongly believe that the competencies can be woven into existing undergraduate medical curricula through purposeful integration and collaboration, including with other specialties.
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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 | 0.073 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".