The Real World Journey of Implementing Fall Prevention Best Practices in Three Acute Care Hospitals: A Case Study
Bibliographic record
Abstract
BACKGROUND: Globally, falls are the second leading cause of unintentional injury. In Canada, falls that occur in hospitals have been ranked second as an area of patient safety concern. Many Canadian hospitals seeking to achieve patient safety, accreditation and resource containment goals are implementing evidence-based practices in fall prevention. However, best practices are reported to be only variably effective in reducing hospital fall rates, indicating a potential gap in our understanding of the implementation process. This study was designed to provide insight into the real world of implementation of best practices in fall prevention in acute care Canadian hospitals. APPROACH: Using case study methodology, ninety-five administrative and point-of-care nurses at three hospitals participated in interviews or focus groups and provided documents and artifacts that described their implementation of a falls prevention guideline. FINDINGS AND IMPLICATIONS: Four recommendations with potential to guide others in fall prevention were identified: (1) the need to listen to and recognize the expertise and clinical realities of staff, (2) the importance of keeping the implementation process simple, (3) the need to recognize that what seems simple becomes complex when meeting individual patient needs, and (4) the need to view the process as one of continuous quality improvement.
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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 | 0.007 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".