Falls in an acute care hospital as reported in the adverse event management system
Bibliographic record
Abstract
Background: The increasing number of falls in hospitals precipitates the need to collect and analyze falls data. Hospital falls data have been captured through staff documentation and incident reporting systems. Objective: The purpose of this study was to identify the variables associated with falls and injurious falls in an acute care hospital over the five years from the implementation of the Adverse Event Management System (AEMS). A secondary purpose was to identify problems associated with the AEMS.Methods: Falls data recorded in the AEMS system from February 2009 to February 2014 were analyzed to observe trends of falls and contributing factors occurring in various hospital units.Results: A total of 7,721 falls occurred during the study period. The highest frequency of the falls (901) occurred between 10:00 a.m. and 12:00 p.m. There were 2,275 falls which resulted in an injury. Both total fall and injurious fall rates were highest in Medicine inpatient units and lowest in Ambulatory outpatient units. The falls rate was 4.5 falls per 1,000 patient days in 2009 and 4.4 falls per 1,000 patient days in 2014. The prevalence of falls varied among nursing unit types and the time of day but the fall rate across the hospital did not change over the five year period.Conclusions: Continuous evaluation of falls data and improved staff education is recommended to help reduce falls in acute care hospitals. A province-wide database registry should be considered for future research on incident reporting.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".