Bedrail Use in English and Welsh Hospitals
Bibliographic record
Abstract
OBJECTIVES: To explore rates of bedrail use, nurses' rationale, and factors related to bedrail use. DESIGN: An overnight observational study of patient and equipment characteristics related to bedrail use, analyzed using a logistic regression model. SETTING: A stratified random sample of seven organizations, drawn from 167 organizations providing acute general hospital care in England and Wales during 2006. PARTICIPANTS: One thousand ninety-two inpatients on adult inpatient wards observed at night. MEASUREMENTS: Categorical data on bedrail use related to bed type, mattress type, patient age, nurses' description of patients' mobility and confusion, and nurses' rationale for bedrail use or nonuse. RESULTS: Approximately one-quarter of patients had full bedrails raised at night; prevention of falls was the nurses' main rationale. Full bedrail use was much more likely to occur in patients who nurses described as immobile and very or slightly confused. Older patients appeared no more likely to be given bedrails than younger patients after adjusting for individual patient and equipment factors. CONCLUSION: Bedrail use varied significantly between organizations and could not be explained by differences in nurses' description of patients' mobility and confusion levels, equipment, or policy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".