Fear of Falling in Older Adults: A Scoping Review of Recent Literature
Bibliographic record
Abstract
BACKGROUND: Fear of falling (FOF) is prevalent among older adults and associated with adverse health outcomes. Over recent years a substantial body of research has emerged on its epidemiology, associated factors, and consequences. This scoping review summarizes the FOF literature published between April 2015 and March 2020 in order to inform current practice and identify gaps in the literature. METHODS: A total of 439 articles related to FOF in older adults were identified, 56 selected for full-text review, and 46 retained for data extraction and synthesis. RESULTS: The majority of included studies were cross-sectional. Older age, female sex, previous falls, worse physical performance, and depressive symptoms were the factors most consistently associated with FOF. Studies that measured FOF with a single question reported a significantly lower prevalence of FOF than those using the Falls Efficacy Scale, a continuous measure. FOF was associated with higher likelihoods of future falls, short-term mortality, and functional decline. CONCLUSIONS: Comparisons between studies were limited by inconsistent definition and measurement of FOF, falls, and other characteristics. Consensus on how to measure FOF and which participant characteristics to evaluate would address this issue. Gaps in the literature include clarifying the relationships between FOF and cognitive, psychological, social, and environmental factors.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".