Teaching Frailty to Medical Residents: A Needs Assessment Among Geriatrics Faculty
Bibliographic record
Abstract
BACKGROUND: Knowledge of frailty is essential for meeting the Accreditation Council for Graduate Medical Education core competencies for US trainees. The UK General Medical Council requires that frailty be included in undergraduate and graduate medical education curricula. Trainees are expected to appropriately modify care plans and help make patient-centered decisions, while incorporating diagnostic uncertainty, such as frailty, in older adults. Little is known about current needs for frailty instruction in graduate medical education in the US and beyond. OBJECTIVE: We sought to capture faculty perceptions on how frailty should be defined and identified, and what aspects and level of detail should be taught to residents. DESIGN: The authors developed a 4-item short response questionnaire, and faculty had the option to respond via electronic survey or via semi-structured interviews. SETTING AND SUBJECTS: Respondents included 24 fellowship-trained geriatricians based at 6 different academic medical centers in a single urban metropolitan area. METHODS: An invitation to participate in either an electronic survey or semi-structured virtual interview was e-mailed to 30 geriatricians affiliated with an academic multi-campus Geriatric Medicine fellowship. Responses were transcribed and coded independently by two authors. RESULTS: Responses were received from 24 geriatricians via a combination of digital questionnaires (n=18) and semi-structured online interviews (n=6), for a response rate of 80%. Responses revealed significant diversity of opinion on how to define and identify frailty and how these concepts should be taught. CONCLUSIONS: As frailty is increasingly incorporated into clinical practice, consensus is needed on how to define and teach frailty to residents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".