Assessing Geriatric Competencies in Residents: Validating the 5Ms Dimensions
Bibliographic record
Abstract
ABSTRACT Background Despite undergraduate training in geriatric care, gaps persist throughout residency, highlighting limitations of current assessment methods in evaluating medical expertise across geriatric dimensions. Objective We developed a case-based assessment using the geriatric 5Ms framework (Mind, Mobility, Medications, Multicomplexity, Matters Most), aligned with undergraduate objectives and North American internal medicine milestones. We present feasibility data and preliminary validity evidence of using the geriatric 5Ms framework to evaluate residents’ geriatric medical expertise. Methods During a 2023 mandatory academic session at a single site, 68 first- to third-year internal medicine residents were randomly assigned to complete assessment and management plans for 3 of 6 geriatric cases within 1 hour. Two blinded educators rated performance on 5Ms dimensions and non-geriatric medical expertise using a 3-level rating scale (0 to 2). We collected feasibility data (logistical integration, participation rates, time to design cases, rate responses) and validity evidence, based on Messick’s framework, in part through a post-assessment questionnaire. Results Sixty-five residents completed 3 cases each, and 3 residents completed 2 cases each, resulting in 201 total cases, each integrating all 5Ms dimensions. Scores across the 5Ms dimensions ranged from 0.8 to 1.3, indicating partial assessment and management. All 5Ms dimensions (mean=1.1, SD=0.3) scored significantly lower than non-geriatric medical expertise (mean=1.5; SD=0.3; t (64)=9.58; P <.001). Interrater reliability was moderate to strong (ICC=0.67-0.85, P <.001). Most residents rated the cases (59 of 67, 88%; mean=4.4; SD=0.7) and the assessment (56 of 67, 84%; mean=4.1; SD=0.7) as representative of clinical practice. Conclusions A case-based assessment using the geriatric 5Ms framework demonstrated feasibility and preliminary validity for evaluating residents’ geriatric medical expertise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".