Comprehensive Falls Analysis at a Long-Term Care Facility: Trends and Recommendations
Bibliographic record
Abstract
Falls among seniors 65 years of age and older have become a significant health issue in Canada. Residents living in long-term care facilities are a population of particular concern. This issue has emphasized the need for quality improvement initiatives aimed at falls prevention to enhance the quality of life and care for those directly impacted. For my NURS 479 leadership course, I conducted a comprehensive 2018 falls analysis for a long-term care facility in Edmonton. Data was collected and analyzed using Microsoft Excel looking into trends and recommendations for improvement. A falls tracking tool that aligned with Alberta Health Services reporting requirements as well as current best practices was also developed. This project was concluded with a presentation to relevant stakeholders and final products were intended to improve the facility’s fall reduction program and inform future quality improvement projects at the site. In this presentation, I will be sharing my project findings and final deliverables. This project was developed in collaboration with my coaches (director of care and nurse educator) and other health care staff at the site as well as my course faculty mentor. Faculty Mentor: Tanya Paananen Department: Nursing
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".