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 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 | 0.022 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".