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Record W4400316489 · doi:10.1136/bmjopen-2023-081645

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

2024· article· en· W4400316489 on OpenAlexaffabout
Orsola Gawronski, Christopher S. Parshuram, Corrado Cecchetti, Emanuela Tiozzo, Leah Szadkowski, Marta Luisa Ciofi degli Atti, Karen Dryden‐Palmer, Immacolata Dall’Oglio, Massimiliano Raponi, Ari R. Joffe, George Tomlinson

Bibliographic record

VenueBMJ Open · 2024
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity Health NetworkUniversity of AlbertaToronto General HospitalHospital for Sick Children
Fundersnot available
KeywordsMedicineRelative riskStaffingConfidence intervalHazard ratioEmergency medicineRate ratioCluster randomised controlled trialCluster (spacecraft)PediatricsFamily medicineRandomized controlled trialNursingInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.055
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.240
GPT teacher head0.529
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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