Case Analysis of Factors Contributing to Patient Falls
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Falls are a constant risk for patients in acute-care hospitals, which can lead to serious consequences. The purpose of this study was to examine hospital fall case studies and to learn the contributing factors for patient falls. This was achieved by conducting a secondary analysis of 11 fall case studies obtained from two previous studies. The fall cases used the Senior Falls Investigative Methodology (SFIM) approach, which provided detailed analysis of the circumstances surrounding the falls. A total of 549 contributing factors were identified in the 11 case studies, where major categories were classified according to the four different layers of defenses using Reason's Swiss Cheese Model of Accident Causation (organizational factors, supervision, preconditions, and unsafe acts). Hospital policies, reduced supervision, disease processes, the environment, and patients transferring without assistance dominated the reasons for increased risk. Additional strategies were recommended for all layers of defense to reduce patient falls.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it