Designing and Implementing a Zero Harm Falls Prevention Program
Bibliographic record
Abstract
BACKGROUND: Inpatient falls with harm have severe implications on patients and the health care system. PURPOSE: We implemented a zero harm approach to falls prevention, which aimed to reduce falls with injury by 25% within 1 year. METHODS: We implemented a multifaceted and multidisciplinary quality improvement falls prevention strategy that included facilitating organization-wide education, adopting the Morse Fall Risk Assessment tool, displaying real-time unit-specific falls rates, and implementing a transparent root-cause analysis process after falls. Our outcome measure was falls with injury per 1000 patient-days. RESULTS: We observed a decrease in the rate of patient falls with injury from 2.03 (baseline period) to 1.12 (1 year later) per 1000 patient-days. We also observed increases in awareness around falls prevention and patient safety incident reporting. CONCLUSIONS: Our zero harm approach reduced falls with injury while improving our patient safety culture.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".