Association of Shift-Level Organizational Factors with Nosocomial Infection in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
Objective: To evaluate the association between shift-level organizational data (unit occupancy, nursing overtime ratios [OTRs], and nursing provision ratios [NPRs]) with nosocomial infection (NI) among infants born very preterm in the neonatal intensive care unit (NICU). Study design: weeks of gestation admitted to 3 tertiary-level NICUs in Quebec between 2014 and 2018. Patient characteristics and outcomes (NIs) were obtained from the Canadian Neonatal Network database and linked to administrative data. For each shift, unit occupancy (occupied/total beds), OTR (nursing overtime hours/total nursing hours), and NPR (number of actual/number of recommended nurses) were calculated. Mixed-effect logistic regression models were used to calculate aOR for the association of organizational factors (mean over 3 days) with the risk of NI on the following day for each infant. Results: Rate of NI was 11.5% (220/1921). Overall, median occupancy was 88.7% [IQR 81.0-94.6], OTR 4.4% [IQR 1.5-7.6], and NPR 101.1% [IQR 85.5-125.1]. A greater 3-day mean OTR was associated with greater odds of NI (aOR 1.08, 95% CI 1.02-1.15), a greater 3-day mean NPR was associated lower odds of NI (aOR 0.96, 95% CI 0.95-0.98), and occupancy was not associated with NI (aOR, 0.99, 95% CI 0.96-1.02). These findings were consistent across multiple sensitivity analyses. Conclusions: Nursing overtime and nursing provision are associated with the adjusted odds of NI among infants born very preterm in the NICU. Further interventional research is needed to infer causality.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".