Risk Factors for Falling Among Community-Based Seniors
Bibliographic record
Abstract
BACKGROUND: Falling constitutes a significant risk to the health and well-being of seniors. Although a number of risk factors have been established within the literature for falling, limited work has differentiated risk factors for 1-time versus recurrent or multiple fallers. METHODS: The purpose of this research was to examine 2 relationships: (1) the risk factors for nonfallers versus fallers (1+ falls); and (2) the risk factors for nonfallers/1-time fallers versus multiple fallers (2+ falls). All participants (n = 453) were subjects within 5 different fall intervention programs funded through the Falls Prevention Initiative sponsored by Health Canada and Veterans Affairs Canada. In total, 5 project sites funded in Ontario conducted independent fall intervention programs. At the onset of their programs and at the completion of their programs, each project site assessed all of their subjects or a predetermined number of seniors (if the subject pool was extensive) using 2 instruments, namely the interRAI Community Health Assessment and the Berg Balance Scale, so that comparisons could be made between sites. RESULTS: Of the 453 individuals, 67% of the sample was classified as nonfallers, with 33% classified as experiencing 1 or more falls. Risk factors significant within the model examining nonfallers versus 1+ fallers included increased medication use and a previous history of falling. For the second analyses, examining 0 falls/1 fall versus recurrent fallers, the following factors were associated with increased risk: medication use, previous history of falling, and compromised activities of daily living (ADL). Fourteen percent of the sample experienced 2+ falls. CONCLUSIONS: It is important to distinguish fallers based on fall status because recurrent or multiple fallers are more likely to benefit from fall prevention efforts. Using a standardized and comprehensive tool such as the interRAI-CHA would assist researchers in making comparisons between different research groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".