The development of an Assessment and Intervention Falls Guide for older hospitalized adults with cardiac conditions
Bibliographic record
Abstract
BACKGROUND: Older patients with chronic cardiac conditions are more vulnerable to falls and injuries. Cardiovascular conditions, prevalent in older people, are also the frequent cause of potentially harmful fall injuries among this group. The need to identify the fall risk-related factors that cluster with arrhythmia and syncope is relevant as it will potentially reduce patients' risk for falls and fall injuries. AIM: The paper describes the process taken to design, develop and implement a practice-change initiative that specifically focuses on cardiac-related falls and injuries. PROCESS: A review of best practice guidelines, related studies and patients' profiles from chart audits were utilized to obtain evidence-based information to develop this assessment and intervention falls guide. Prior to the development of this guide, the charts of six patients were reviewed to assess specific data including age, history of falls, type of injury, cognitive function and underlying medical conditions. The developed Assessment and intervention falls guide was utilized with seven patients in the Cardiology Unit who were admitted with diagnosis of syncope and atrial fibrillation to assess their risk for falls. CONCLUSION: The project demonstrated an evidence-informed process that was used to design and implement this assessment tool and a change in nursing practice. It also provided information about the prevalence of cardiac-related risk fall factors that were utilized in the development of this Assessment and Intervention Falls Guide for elderly people.
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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 | 0.000 |
| 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 | 0.000 |
| 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".