FALLS AMONG ASSISTED LIVING RESIDENTS: RESULTS FROM THE 2016 NATIONAL STUDY OF LONG-TERM CARE PROVIDERS
Bibliographic record
Abstract
Fall-related injuries among older adults are common and expensive; can result in hospitalizations, functional decline, and nursing-home placement; and, are more common in institutionalized settings. Even falls not resulting in injury can increase fear of falling, future fall risk, depression, and social isolation. Falls in assisted living and similar residential care communities (RCCs) have been less studied than in nursing home settings. A 2010 Cochrane systematic review found that 20% to 30% of falls in long-term care facilities are preventable. We present results from the National Study of Long-Term Care Providers conducted by the National Center for Health Statistics. In 2016, 22% of current RCC residents had a fall in the prior 90 days, representing 175,000 RCC residents in the United States; in 20% of RCCs, more than one-quarter of residents had a fall. Among residents with a fall, 15% had a fall-related injury; in 7% of RCCs, more than one-quarter of residents sustained a fall-related injury. Among residents with a fall, 19% went to a hospital as a result of the fall; in 11% of RCCs, more than one-quarter of residents went to a hospital as a result of the fall. Four-tenths of RCCs reported using a fall risk assessment tool as standard practice with every resident; almost three-fourths of RCCs report using some type of formal fall reduction intervention. Results will be further examined by selected resident case-mix and other RCC characteristics. Results may inform strategies to target RCCs that might benefit from evidence-based fall reduction interventions.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".