Adopting the Fall Tailoring Interventions for Patient Safety (TIPS) Program to Engage Older Adults in Fall Prevention in a Nursing Home
Bibliographic record
Abstract
BACKGROUND: Falls are the leading cause of injury-related hospitalizations and deaths among older adults globally. LOCAL PROBLEM: About 24% of Canadian nursing home residents fall annually. This quality improvement project evaluated the impact of the Fall Tailoring Interventions for Patient Safety (TIPS) program on preventing falls and fall-related injuries among older adult nursing home residents in a subacute care unit in Canada. METHODS: We used the Standards for Quality Improvement Reporting Excellence (SQUIRE) 2.0 guidelines for reporting. The intervention site is a 15-bed subacute care unit within a government-funded nursing home. INTERVENTION: The Fall TIPS program was adapted to a nursing home setting to prevent falls. It provides fall prevention clinical decision support at the bedside. RESULTS: The rates of falls and injuries decreased after implementing the Fall TIPS intervention. CONCLUSION: Engaging nursing home older adult residents in fall prevention is crucial in translating evidence-based fall prevention care into clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".