COMPARATIVE ANALYSIS OF FALL RATES AMONG COMMUNITY-OLDER ADULTS IN SIX COUNTRIES
Bibliographic record
Abstract
Abstract The number of older people who desire to live in their homes is rising. So, this study aimed to evaluate fall rates in community-dwelling older adults across six English-speaking countries. This cross-sectional survey was conducted with 114 older adults residing in Canada, the USA, Australia, Ireland, New Zealand, the UK, and Northern Ireland. Participants recruited online through social media and passive snowball sampling completed self-rated fall risk (FRQ) and Activities-specific Balance Confidence (ABC-6) surveys, along with sociodemographic and health-related questions. Canadian and Irish respondents comprised the highest and lowest proportions of the sample, at 37% and 9%, respectively. The mean age was 67 (SD = 7.8), with a balanced gender distribution. Chronic diseases, notably joint pain and abnormal blood pressure, were reported by 25.4% of respondents. 44.8% of respondents reported falls in the past year, with 35.1% reporting falls in the past six months. Multiple falls were reported by 25.2% of respondents, with 45.8% reporting fractures primarily in the hands, forearms, or arms. Fall rates varied significantly among the countries: Canada (54.1%), USA (44.4%), Australia (53.8%), Ireland (40%), New Zealand (41.7%), and the UK and Northern Ireland (23.5%). Geographic, demographic, and methodological factors influence these rates. Higher fall rates in Canada may be due to an aging population and adverse weather conditions. The study’s findings have direct implications for research, clinical practice, and education. Recommendations for a prospective design with a large sample size are essential for a more comprehensive understanding of falls rate and effective prevention of falls.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".