A Workshop for Interprofessional Trainees Using the Geriatrics <scp>5Ms</scp> Framework
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Interprofessional trainees need geriatrics training to prepare them to care for our aging population. Team-based care will help them be ready to work in an Age-Friendly Health System. The Geriatrics 5Ms provides a framework to engage learners in five main domains of caring for older adults from an interprofessional perspective: Mobility, Mind, Medications, Multicomplexity, and what Matters Most. DESIGN: We created a half-day workshop for interprofessional trainees using the Geriatric 5Ms framework to increase their preparedness in caring for older adults as part of an interprofessional team. SETTING: The New England Geriatric Research Education and Clinical Center. PARTICIPANTS: A total of 66 trainees from 10 professions. INTERVENTION: After introductory sessions on careers in aging, participants engaged in an interactive session to learn about the professions represented. They then formed interprofessional groups to discuss a patient case using the Geriatrics 5Ms framework with a modified jigsaw format. MEASUREMENTS: Trainees were surveyed before and after the workshop on their attitudes toward careers in aging, understanding of skills and training paths of other professions, and familiarity with the Geriatrics 5Ms framework. RESULTS: Overall, 97% of the trainees rated the workshop highly. Trainee ratings significantly increased in the areas of understanding of other professions, and familiarity and applicability of the Geriatrics 5Ms, particularly for nonphysicians. CONCLUSION: A workshop for interprofessional trainees using the Geriatrics 5Ms framework increased the readiness of trainees to care for older adults as part of an interprofessional team. This workshop offers a promising model for needed interprofessional geriatrics education. J Am Geriatr Soc 68:1857-1863, 2020.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".