Preventing Inpatient Falls Using a Patient Centered Fall Prevention Toolkit
Bibliographic record
Abstract
Purpose: The purpose of this quality improvement project was to evaluate an educational PowerPoint session describing the Fall T.I.P.S. Patient-Centered Fall Prevention Toolkit as an effective evidence-based method to increase patient knowledge of fall risk and clearly communicates the mobility needs of the patient in the acute care hospital. Background: Falls account for 70% of all hospital accidents and are the leading cause of injury and death by injury in adults over the age of 65. There is a significant increase in financial costs, including an increase in tests to confirm an injury, treatment of the injury, and longer lengths of stay. Despite the numerous studies on the importance of fall prevention programs, standards of care on fall prevention strategies vary between different healthcare organizations, guidelines are inconsistently implemented, and the incidence of falls remains high in hospitalized patients Methods: This quality improvement project included a group of 10 volunteer patients from the UA TLC who were 65 years old or older. The Ottawa Model of Research Use guided the development, implementation, and evaluation of the educational PowerPoint session by using a quantitative design with a pretest and posttest format. A Zoom meeting format was utilized to implement this project where the participants were asked to use a link in the chat box to anonymously complete the surveys. Results: Data collection took place prior to the educational PowerPoint session and immediately following, with all 10 participants completing both pretest and posttest surveys. There was an increase in the knowledge of what places someone at risk for falls while in the acute care hospital. Conclusion: This quality improvement project helped to increase knowledge of best practices and thereby has the potential to improve patient outcomes. Educating patients on the risk of falls and communicating the mobility needs of the patient to all members of the health care team including the patients and their family members is key to reducing the risk of inpatient falls.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".