Factors and Costs Associated With the Use of Registered Nurse Overtime in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
Background: Planning the number of registered nurses (RN) per shift in the neonatal intensive care unit (NICU) is a constant stressor and overtime is often used to assure adequate nurse to patient ratios at high costs.Aim: To identify the factors associated with shift-to-shift variations in the use of RN overtime in the NICU and assess the economic impacts of reducing overtime.Methods: We developed a two-year retrospective study in a NICU (CHU de Québec, Level 3 unit, capacity of 51 beds).Detailed administrative data for each shift of the day (night, day, evening) was collected.Non-modifiable organizational factors included patient volume, patient acuity, number of admissions, season, days of the week and work shift.The modifiable factors included the paid hours not at the bedside and the implementation of a bundle to reduce RN overtime (increase in full-time nurse positions and conversion of 10% of RN to regular 12hour shifts).Multivariate linear regression models were used to assess the association between organizational factors and RN overtime per shift.Results: A total of 2184 shifts were included.Mean RN overtime per shift was 9.5±10.4h corresponding to 4.7±5.2% of total hours worked per shift.RN overtime use was influenced by non-modifiable factors including unit occupancy, season and the number of acute patients.Paid hours not at the bedside were associated with overtime.Also, the implementation of a bundle to reduce RN overtime brought the mean RN overtime from 11.7±11.2h to 6.5±8.5 h (p<0.001).This was associated with a reduction in nursing costs per patient day [386±10 $ vs. 381±7 $ (p<0.001)].That corresponded to a yearly 102,948$ cost reduction.Conclusion: Reducing RN overtime in the NICU is associated with cost reduction.
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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".