Implementing Tai Chi Exercise in Long-Term Care to Reduce Falls
Bibliographic record
Abstract
BACKGROUND: Falls are a frequent occurrence in older adults in long-term care facilities. LOCAL PROBLEM: At our long-term care facility, the percentage of patients who fell increased from 45% in 2021 to 68% in 2022, indicating a need for an evidence-based solution. METHODS: We used an evidence-based quality improvement framework to pilot a tai chi exercise program. INTERVENTIONS: Residents were invited to participate in the Tai Ji Quan: Moving for Better Balance program for 12 weeks. Classes were 30 minutes long and included a 5-minute warm-up and 5-minute cooldown. RESULTS: Seventy-five residents participated in the tai chi program. There was a significant 32.3% reduction in falls ( P =.001). Residents' fall risk scores decreased 14% ( P < .001). CONCLUSIONS: Implementing a tai chi exercise project may affect falls and decrease the overall fall risk.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".