Risk Factors for Falling Among Community-Based Seniors Using Home Care Services
Bibliographic record
Abstract
BACKGROUND: Despite the plethora of information concerning risk factors for falls, limited research efforts have focused on the issue of the differences in risk factors for falls based on fall status, or more specifically one-time versus chronic/recurrent fallers. Given that multiple falls have been found to be associated with negative outcomes, such as an increased risk of institutionalization, more research in this area is warranted. METHODS: The purpose of this investigation was to determine the risk factors for nonfallers versus fallers (1+ falls), and for nonfallers/one-time fallers versus recurrent fallers (2+ falls). All participants (N = 2304) in this study were receiving home care services from 10 community-based agencies (Community Care Access Centres) in Ontario, Canada. The Minimum Data Set-Home Care (MDS-HC) is an assessment instrument that covers several key domains, such as service use, function, health, and social support. Nurses trained to administer the MDS-HC assessed each of the participants within their homes. RESULTS: Of the 2304 participants in the study, 27% fell one or more times, and 10% experienced multiple falls (2+ falls). In the two final logistic regression models for risk of falling (0 falls vs 1+ falls) and multiple falling (0 falls/1 fall vs 2+ falls), the independent variables that remained significant included gender, gait, environmental hazards, and the Changes in Health, End Stage Disease and Signs and Symptoms of Medical Problems Scale. Also significant in the model for multiple falls was the Cognitive Performance Scale, Parkinson's disease, and perceived health status. CONCLUSIONS: Overall, distinguishing individuals into different fall status classifications is important from a clinical perspective, as it is the recurrent faller who would benefit to the greatest extent from fall prevention efforts and from the negative outcomes associated with multiple falls (i.e., mortality). One of the most significant barriers in determining risk factors for falls is the lack of consistency in the variables/tools used in the research. As such, utilizing a standardized tool, such as the MDS-HC, would assist researchers in making comparisons between different settings.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".