Factors contributing to frailty: literature review
Bibliographic record
Abstract
AIM: This paper presents a review of theoretical and research literature in order to identify the factors contributing to frailty. BACKGROUND: Frailty is a multifaceted gerontological concept that lacks a clear definition, but may result from an identifiable homogeneous cluster of bio-psycho-social-spiritual factors. METHOD: A total of 134 articles were identified through a search of the MEDLINE (1966 to July 2004), CINAHL (1982 to July 2004), PsychInfo (1985 to July 2004) and Ageline (1995 to July 2004) databases. Each article was reviewed to determine its fit with inclusion/exclusion criteria. Seven research and 11 theoretical articles were retained and further reviewed for methodological quality using a validity tool. FINDINGS: Seventeen different definitions of frailty were identified. Regardless of the differing definitions, common contributing factors could be identified. Physical, cognitive/psychological, nutritional and social factors, as well as ageing and disease, were evident in both the theoretical and research literature. CONCLUSIONS: Although there is strong agreement that a relationship exists between a cluster of factors and frailty, designation of the factors as contributors or outcomes of frailty differs. Without a clear explanatory theory of the path from contributors to frailty to outcomes, research will continue to produce confusing results. A theoretical framework that includes bio-psycho-social-spiritual factors as contributors to frailty is recommended as the most useful framework for gerontological nursing.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".