In-house, overnight physician staffing: A cross-sectional survey of Canadian adult and pediatric intensive care units*
Bibliographic record
Abstract
OBJECTIVE: Physician staffing is an important determinant of patient outcomes following intensive care unit (ICU) admission. We conducted a national survey of in-house after-hours physician staffing in Canadian ICUs. DESIGN: : Cross-sectional survey. SETTING: Canadian adult and pediatric ICUs. PARTICIPANTS: ICU directors. INTERVENTIONS: ICU directors of Canadian adult and pediatric ICUs were surveyed to describe overnight staffing by interns, residents, critical care medicine trainees, clinical assistants, and ICU physicians in their ICUs. MEASUREMENTS AND MAIN RESULTS: Data were collected regarding hospital and ICU demographics and ICU staffing. For ICUs with in-house overnight physicians, we documented physician experience, shift duration, and clinical responsibilities outside the ICU. We identified 98 Canadian ICU directors, of whom 88 (90%) responded. Dedicated in-house physician coverage overnight was reported in 53 (60%) ICUs, including 13 (15%) in which ICU staff physicians stayed in-house overnight. Compared with ICUs without in-house physicians, those with in-house physicians had more ICU beds (15 vs. 8.5, p=.0001) and fewer ICU staff physicians (5 vs. 7, p=.03). For the 271 physicians who provide overnight staffing, the median level of postgraduate experience was 3 yrs (range, <1 yr, >10 yrs); 129 (48%) had <3 months of ICU experience. Most shifts (83%) were >20 hrs long. CONCLUSIONS: In-house overnight physician staffing in Canadian ICUs varies widely. Only a minority of ICUs comply with the 2003 Society of Critical Care Medicine guidelines for adult ICUs recommending continuous in-house staffing by ICU staff physicians. The duration of most ICU shifts raises concern about workload-associated fatigue and medical error. The impact of current nighttime staffing requires further evaluation with respect to patient outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".