A scoping review: Nurses' workload in patients for fall risk screening
Bibliographic record
Abstract
Half of all falls result in injury, of which 10% are seriously impacted. Direct medical expenses resulting from falls amount to nearly $30 billion annually. Nurses must engage cognitive, affective, and actions that put patient safety first. The breadth of the nurse's role can allow for the risk of errors in service. This scoping review is prepared to discuss the effect of nurse workload in screening the risk of falls on patients. The method used here is scoping review which is used to develop a 'map' regarding the effect of nurse workload in screening the current patient's fall risk. The search databases used in this writing include: SINTA, PubMed, EBSCOhost, and SagePub. The search was conducted on October 16, 2023 and the included studies were research taken in 2013-2023. The authors found seven scientific journals that discuss the effect of workload in screening patient fall risk. The four journals came from Indonesia, one journal each from Brazil, Taiwan, and Canada. Workload is consistently associated with suboptimal fall risk screening. This is because nurses will be busy doing other things, so screening and monitoring patients from the risk of falling cannot be done properly.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".