Factors Associated With Falls Among Residents Living in Long‐Term Care Homes in Ontario
Bibliographic record
Abstract
INTRODUCTION: The prevalence of falls in Ontario-based long-term care homes is above the provincial benchmark. There is limited research exploring the reason for such a variation. The research question guiding this study was: What are the risk factors for falls among all residents in Ontario's LTC homes? METHODS: A retrospective, population-based study was conducted using Minimum Data Set assessments for all residents of long-term care in Ontario between April 2019 and March 2020. Binomial logistic regression analysis was used to determine the significance of the relationship of selected variables to falls. RESULTS: Findings identified a significant relationship between several variables that were not previously found in the existing literature and falls. CONCLUSION: This study has important implications for clinicians and researchers globally as they aim to better understand the increased prevalence of falls in older adults living in residential care. IMPLICATIONS FOR PRACTICE: Clinicians are encouraged to consider alternatives to high-risk medications and closely monitor residents on these medications, implement harm reduction strategies for residents with responsive behaviors, and routinely assess residents for bowel incontinence, cognitive decline, or increased care needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".