Perceptions and Understandings of Frailty Language: A Scoping Review
Bibliographic record
Abstract
Abstract Diagnosing and responding to frailty in older adult populations is of growing interest for health care professionals, researchers and policymakers. Preventing frailty has the potential to improve health outcomes for older adults, which in turn has significant implications for health care systems. However, little is known about how older people understand and perceive the term “frailty”, and what it means for them to be designated as frail. To address this concern, a scoping review was undertaken to map the breadth of primary research studies that focus on community-dwelling older adults’ perceptions and understanding of frailty language, as well as explore the potential implications of being classified as frail. Searches were conducted in MEDLINE, Ageline, PsychInfo, CINAHL and EMBASE databases for articles published between January 1994 and February 2019. 4639 articles were screened and ten articles met the inclusion criteria, detailing eight primary research studies. Using content analysis, three core themes were identified across the included studies. These themes included: 1) understanding frailty as a multi-dimensional concept and inevitable consequence of aging, 2) perceiving frailty as a generalizing and harmful label; and 3) resisting and responding to frailty. Recommendations stemming from this review include the need for health care professionals to use person-centered language with older adults, discuss the term frailty with caution, and be aware of the potential consequences of labeling a person as frail. Importantly, this review demonstrates that for frailty interventions to be successful and meaningful for older adults, ongoing and critical examination of frailty language is necessary.
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 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 | 0.022 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".