Overnight staffing in Canadian neonatal and pediatric intensive care units
Bibliographic record
Abstract
Aim: Infants and children who require specialized medical attention are admitted to neonatal and pediatric intensive care units (ICUs) for continuous and closely supervised care. Overnight in-house physician coverage is frequently considered the ideal staffing model. It remains unclear how often this is achieved in both pediatric and neonatal ICUs in Canada. The aim of this study is to describe overnight in-house physician staffing in Canadian pediatric and level-3 neonatal ICUs (NICUs) in the pre-COVID-19 era. Methods: A national cross-sectional survey was conducted in 34 NICUs and 19 pediatric ICUs (PICUs). ICU directors or their delegates completed a 29-question survey describing overnight staffing by resident physicians, fellow physicians, nurse practitioners, and attending physicians. A comparative analysis was conducted between ICUs with and without in-house physicians. Results: We obtained responses from all 34 NICUs and 19 PICUs included in this study. A total of 44 ICUs (83%) with in-house overnight physician coverage provided advanced technologies, such as extracorporeal life support, and included all ICUs that catered to patients with cardiac, transplant, or trauma conditions. Residents provided the majority of overnight coverage, followed by the Critical Care Medicine fellows. An attending physician was in-house overnight in eight (15%) out of the 53 ICUs, seven of which were NICUs. Residents participating in rotations in the ICU would often have rotation durations of less than 6 weeks and were often responsible for providing care during shifts lasting 20-24 h. Conclusion: Most PICUs and level-3 NICUs in Canada have a dedicated in-house physician overnight. These physicians are mainly residents or fellows, but a notable variation exists in this arrangement. The potential effects on patient outcomes, resident learning, and physician satisfaction remain unclear and warrant further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".