Twenty-four–Hour Intensivist Presence: A Pilot Study of Effects on Intensive Care Unit Patients, Families, Doctors, and Nurses
Bibliographic record
Abstract
RATIONALE: Around-the-clock intensivist presence in intensive care units (ICUs) has been promoted as necessary to optimize outcomes. Little data have addressed how it affects the multiple stakeholders in such care. OBJECTIVES: To assess effects of around-the-clock intensivist presence on intensivists, patients, families, housestaff, and nurses. METHODS: This 32-week, crossover pilot trial of two intensivist staffing models, performed in two Canadian ICUs, alternated 8-week blocks of two staffing models: the standard model, where one intensivist worked for 7 days, taking night call from home; and the shift work model, where one intensivist worked 7 day shifts, while other intensivists remained in the ICU at night. MEASUREMENTS AND MAIN RESULTS: Surveys scaled from 0-100 points assessed outcomes for 24 intensivists (primary outcome: burnout); 119 families (satisfaction); 74 nurses (satisfaction with collaboration and communications, role conflict); and 34 housestaff (autonomy, supervision, and learning opportunities). Outcomes for 501 patients included mortality, length of stay, and resource use. Intensivists doing shift work experienced less burnout (-6.9 points; P = 0.04). Adjusted hospital mortality (odds ratio, 1.22; P = 0.44), ICU length of stay (-6 h; P = 0.46), and family satisfaction (0.9 points; P = 0.79) did not differ between staffing models. Under shift work staffing, nurses reported more role conflict (9 points; P < 0.001), whereas nighttime housestaff reported less autonomy, more supervision, but no difference in learning opportunities. CONCLUSIONS: Shiftwork staffing was better for intensivists and most were receptive once they had experienced it. Although there were no evident negative outcomes for patients or families, further evaluation is needed to clarify how around-the-clock intensivist staffing influences the various stakeholders in ICU care, given power considerations in this study. Clinical trial registered with www.clinicaltrials.gov (NCT 01146691).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".