Identifying frailty in primary care: a qualitative description of family physicians’ gestalt impressions of their older adult patients
Bibliographic record
Abstract
BACKGROUND: Many tools exist to guide family physicians' impressions about frailty status of older adults, but no single tool, instrument, or set of criteria has emerged as most useful. The role of physicians' subjective impressions in frailty decisions has not been studied. This study explores how family physicians conceptualize frailty, and the factors that they consider when making subjective decisions about patients' frailty statuses. METHODS: Descriptive qualitative study of family physicians who practice in a large urban academic family medicine center as they participated in one-on-one "think-aloud" interviews about the frailty status of their patients aged 80 years and over. Of 23 eligible family physicians, 18 shared their impressions about the frailty status of their older adult patients and the factors influencing their decisions. Interviews were audio-recorded, transcribed, and thematically analyzed. RESULTS: Four themes were identified, the first of which described how physicians conceptualized frailty as a spectrum and dynamic in nature, but also struggled to conceptualize it without a formal definition in place. The remaining three themes described factors considered before determining patients' frailty statuses: physical characteristics (age, weight, medical conditions), functional characteristics (physical, cognitive, social) and living conditions (level of independence, availability of supports, physical environment). CONCLUSIONS: Family physicians viewed frailty as multifactorial, dynamic, and inclusive of functional and environmental factors. This conceptualization can be useful to make comprehensive and flexible evaluations of frailty status in conjunction with more objective frailty tools.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".