Comparison of Three Nursing Workload Assessment Tools in the Neonatal Intensive Care Unit and Their Association with Outcomes of Very Preterm Infants
Bibliographic record
Abstract
OBJECTIVE: Nursing workload assessment tools are widely used to determine nurse staffing requirements in the neonatal intensive care unit (NICU). We aimed to compare three existing workload assessment tools and assess their association with mortality or morbidity among very preterm infants. STUDY DESIGN: Single-center retrospective cohort study of infants born <33 weeks and admitted to a 52-bed tertiary NICU in 2017 to 2018. Required nurse staffing was estimated for each shift using the Winnipeg Assessment of Neonatal Nursing Needs Tool (WANNNT) used as reference tool, the Quebec Provincial NICU Nursing Ratio (QPNNR), and the Canadian NICU Resource Utilization (CNRU). Poisson regression models with robust error variance estimators were used to assess the association between nursing provision ratios (actual number of nurses/required number of nurses) during the first 7 days of admission and neonatal outcomes. RESULTS: < 0.0001). The NICU nursing provision ratios during the first 7 days of admission calculated using the WANNNT (adjusted risk ratio [aRR]: 0.96, 95% confidence interval [CI]: 0.93-0.99) and QPNNR (aRR: 0.97, 95% CI: 0.95-0.99) were associated with mortality or morbidity. CONCLUSION: Lower nursing provision ratio calculated using the WANNNT and CNRU during the first 7 days of admission is associated with an increased risk of mortality/morbidity in very preterm infants. KEY POINTS: · NICUs use different nursing workload assessment tools.. · We validated three different nursing workload assessment tools used in the NICU.. · Nursing provision ratio is associated the risk of mortality/morbidity in preterm infants..
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".