Nurse staffing, patient turnover and safety climate and their association with in-patient falls and injurious falls on medical acute care units: a cross-sectional study
Bibliographic record
Abstract
Objective: Falls and related injuries remain a considerable health risk for in-patients. Numerous studies link falls with nurse staffing levels, but the results are inconsistent. The purpose of this study was to explore the associations between fall prevalence and injurious falls on medical wards and three unit-level system factors: daily nurse staffing, patient turnover, and safety climate.Methods: Using a cross-sectional design, we conducted a secondary data analysis of data from the Patient Safety and Falls Project. Five medical units in a Swiss university hospital were included, resulting in a data set of 949 days, with daily measures of nurse staffing, patient turnover and falls. The safety climate was measured using a subscale of the Safety Attitudes Questionnaire and analyzed at the unit level including data from 154 nurses. Robust multivariate logistic regression was used to explore nurse staffing, patient turnover, and safety climate’s associations with in-patient falls and fall injuries.Results: After controlling for patient age, length of stay and nursing fulltime equivalents, registered nurse experience showed a significant negative relationship with falls (OR = .83, p < .0001). Patient turnover and safety climate were not significantly associated to falls or fall injuries.Conclusions: By linking nurse staffing variables to in-patient falls and fall injuries, the current study’s findings partly confirm those of previous research. Further investigation will be necessary to isolate key factors influencing the association at the unit level between safety climate and in-patient falls.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".