Factors Associated with Falls in Canadian Long Term Care Homes: a Retrospective Cohort Study
Bibliographic record
Abstract
Background: Half of Canadians living in long-term care (LTC) homes will fall each year resulting in consequences to independence, quality of life, and health. The objective in this study was to analyze factors that contribute to, or are protective against, falls in Canadian LTC homes. Methods: We analyzed of a retrospective cohort of a stratified random sample of Canadian LTC homes in Western Canada from 2011-2017. We accessed variables from the RAI-MDS 2.0 to assess the association of the dependent variable "fall within the last 31-180 days" with multiple independent factors, using generalized estimating equation models. Results: A total of 28,878 LTC residents were analyzed. Factors found to increase the odds of falling were other fractures (OR 3.64 [95% confidence interval; CI 3.27, 4.05]), hip fractures (OR 3.58 [3.27, 3.93]), moderately impaired cognitive skills (OR 2.45 [2.28, 2.64]), partial support to balance standing (OR 2.44 [2.30, 2.57]), wandering (OR 2.31 [2.18, 2.44]). Conclusion: A range of factors identified were associated with falls for people living in LTC homes. Individual physical ability represented the largest group of independent factors contributing to falls. Residents who experience any fracture or an acute change in behaviour, mobility, or activities of daily living (ADL) should be considered at increased risk 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".