Prevalence of falls with minor and major injuries and their associated factors among older adults in long‐term care facilities
Bibliographic record
Abstract
Aims and objectives. The objectives of this study were to determine the prevalence of falls with minor and major injuries and identify their risk factors. Background. Falls among residents of long-term care facilities (LTCF) constitute a significant health issue. Design. This is a secondary analysis of a cross-sectional study carried out among older people (n = 2332). Methods. This is a descriptive study focusing on the secondary analysis of a cross-sectional study carried out with a group of older people (n = 2332) in 28 LTCF in Quebec City, Canada. Research assistants collected original data for each resident from two sources: structured simultaneous interviews with two nurses per unit from each of the homes and a review of the residents' medical files. Results. 7.2% of subjects had a fall leading to minor injuries and 10.1% a fall leading to major injuries. Risk factors associated with fall-related minor injury are young age, male gender and cognitive impairment. Factors associated with fall-related major injury were functional autonomy and length of stay. In further statistical analysis, controlling for functional autonomy, disruptive behaviours and neuroleptic use were found associated with fall-related major injury. Conclusions. This study demonstrates that the factors associated with fall-related minor injury are different from those associated with fall-related major injury. Relevance to clinical practice. This study suggests that nurses working with LTCF residents who are concerned about the prevention of fall-related major injury, may contribute to a reduction in such falls through optimal management of behavioural problems and neuroleptic use.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".