A Multilevel Investigation of Fall Prevention Behavior Among Nursing Staff of South Korean Geriatric Hospitals
Bibliographic record
Abstract
BACKGROUND: There is lack of empirical evidence on whether organizational variables affect the fall prevention behavior of nursing staff working at Korean geriatric hospital. Aim This study aimed to investigate individual and organizational characteristics associated with the fall prevention behavior of nurses and nurse aides. METHODS: A descriptive cross-sectional research design was used. A convenient sample of 426 clinical nurses and nurse aides from 8 geriatric hospitals in South Korea was recruited between October and November 2019. Hierarchical regression analysis was used to estimate the effects of individual- and organization-level predictors. RESULTS: The result indicated that fall prevention self-efficacy (β=0.41, p<.001) was a significant individual-level predictor. At the organizational level, Nurse to nurse aides ratio (β=.21, p=.005) and number of patients per physical therapist (β=-.28, p=.014) were significant predictors. Furthermore, there was a significant change of R2 (p=.034) when organizational variables were included in the regression model. CONCLUSION: To increase fall prevention behavior of nurse and nurse aides, administrators in geriatric hospital should recognize the importance of staffing, such as nurse and physical therapist. Further studies are proposed to investigate the empirical evidence about the association between organizational variables and patient outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".