Fall Risk Factors in Community-Dwelling Older Adults: An Umbrella Review Protocol
Bibliographic record
Abstract
BACKGROUND: Falls are among the leading cause of accidental injury in Canada, with one in three older adults experiencing a fall each year. This leads to disability, long-term pain, loss of independence, reduced quality of life, mental health challenges, and increased mortality risk. Alongside these injuries, falls are a growing national public health concern given their direct cost of over $2 billion a year. Further, with an aging Canadian population, the prevalence and incidence of falls are expected to increase at a rapid rate. A large body of literature has identified risk factors that contribute to falls in older adults, however, to date this literature has been disjointed and often groups together fall risks for different populations. To address these limitations, this review will aim to summarize the evidence identifying risk factors for falls in community-dwelling older adults (60 years). METHODS: We searched seven databases (Medline, Embase, CINAHL, Cochrane Library, PsychINFO, Ageline) for articles that: are systematic reviews (including scoping reviews), include a population of community dwelling older adults (60 years), and report modifiable and non-modifiable factors that have shown to increase the risk for falls in prospective studies. Multiple independent reviewers will screen titles, abstracts, and full text articles, and perform data extraction and quality assessment. Risk factors will be synthesized narratively. Wherever possible, findings will be grouped according to the International Classification of Functional Disability (ICF). ANTICIPATED IMPACT: Given the substantial amount of research conducted in this area, we anticipate that the identified risk factors for falls will cover a wide breadth. Further, our results will identify the risk factors that are of the utmost importance to consider when undertaking fall prevention efforts.
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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 | 0.069 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".