Decreasing Inpatient Falls: A Retrospective Analysis with CNS-Led Interventions
Bibliographic record
Abstract
Inpatient falls are considered 'never events' that may result in increased costs and adversely affect quality of care. This study describes and analyzes characteristics of inpatient falls collected on a ‘Post Fall Huddle/Event Report’ form used by nursing and Risk Management. Data from a convenience sample of 182 falls were collected over a six month period. Analyses included descriptive and regression models on outcomes of injury and length of stay. Incontinence/elimination, antihypertensive medications, higher census, and any medication change, suggested associations with injury or length of stay. Based on the results several strategies were implemented by CNSs to include in-depth review of circumstances surrounding the fall; education of staff for consistency in the fall protocol; reinforcement of purposeful hourly rounding; and the need for increased objectivity on the Post Fall Huddle/Event Report form. Post implementation the average annual number of inpatient falls decreased by 50%.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".