Evaluating associations between patient-to-nurse ratios and mortality, process of care events and vital sign documentation on paediatric wards: a secondary analysis of data from the EPOCH cluster-randomised trial
Bibliographic record
Abstract
OBJECTIVE: To describe the associations between patient-to-nurse staffing ratios and rates of mortality, process of care events and vital sign documentation. DESIGN: Secondary analysis of data from the evaluating processes of care and outcomes of children in hospital (EPOCH) cluster-randomised trial. SETTING: 22 hospitals caring for children in Canada, Europe and New Zealand. PARTICIPANTS: Eligible hospitalised patients were aged>37 weeks and <18 years. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was all-cause hospital mortality. Secondary outcomes included five events reflecting the process of care, collected for all EPOCH patients; the frequency of documentation for each of eight vital signs on a random sample of patients; four measures describing nursing perceptions of care. RESULTS: A total of 217 714 patient admissions accounting for 849 798 patient days over the course of the study were analysed. The overall mortality rate was 1.65/1000 patient discharges. The median (IQR) number of patients cared for by an individual nurse was 3.0 (2.8-3.6). Univariate Bayesian models estimating the rate ratio (RR) for the patient-to-nurse ratio and the probability that the RR was less than one found that a higher patient-to-nurse ratio was associated with fewer clinical deterioration events (RR=0.88, 95% credible interval (CrI) 0.77-1.03; P (RR<1)=95%) and late intensive care unit admissions (RR=0.76, 95% CrI 0.53-1.06; P (RR<1)=95%). In adjusted models, a higher patient-to-nurse ratio was associated with lower hospital mortality (OR=0.77, 95% CrI=0.57-1.00; P (OR<1)=98%). Nurses from hospitals with a higher patient-to-nurse ratio had lower ratings for their ability to influence care and reduced documentation of most individual vital signs and of the complete set of vital signs. CONCLUSIONS: The data from this study challenge the assumption that lower patient-to-nurse ratios will improve the safety of paediatric care in contexts where ratios are low. The mechanism of these effects warrants further evaluation including factors, such as nursing skill mix, experience, education, work environment and physician staffing ratios. TRIAL REGISTRATION NUMBER: EPOCH clinical trial registered on clinical trial.gov NCT01260831; post-results.
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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 | 0.031 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".