"Risk-Taking and Risk of Falls in Community-Dwelling Older Adults: A Scoping Review"
Bibliographic record
Abstract
Background: Risk-taking behaviors have emerged as a target for fall prevention. However, the risk-taking concepts are complex, and several approaches exist to identify risk-taking behaviors. In addition, studies of fall-related risk-taking behaviors have not yet been systematically evaluated.Methods: This scoping review was conducted in accordance with Joanna Briggs Institute’s methodology for scoping reviews. Six electronic databases were searched to identify studies published between 2000 and 2020. Studies were included in our review if they were conducted on community-dwelling older adults (≥ 65 years) and discussed fallrelated risk-taking behaviors. Data extraction and analyses were completed using a table developed a priori by the research team.Results: Self-reported behaviors using qualitative methodology were the most common approach to identifying risktaking behaviors in community-dwelling older adults. Generally, older adults are aware of their fall risk and tend to adopt behaviours to help mitigate it. However, older adults also described moments of deliberate risk-taking driven by the potential benefits of this behavior. Factors associated with risk-taking include an individual’s abilities, personal values, and physical and social environment.Conclusion: This review demonstrated that fall-related risk-taking behaviors are a highly individualized concept influenced by a number of factors. Therefore, future research should evaluate how risk appraisal, risk attitudes, and risk propensity predict fall-related risk-taking behaviors in community-dwelling older adults.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".