Interprofessional Team Staffing in U.S. Intensive Care Units
Bibliographic record
Abstract
Abstract Rationale There is a paucity of data, and no consensus, about the composition of interdisciplinary teams of healthcare workers (HCWs) who provide care in intensive care units (ICUs). Objectives To delineate the nature and variation of HCW staff composition in U.S. adult ICUs before the COVID-19 pandemic. Methods A national survey of 574 adult ICUs inquired about ICU staffing. Two sets of survey items asked about 1) “availability to provide care” in ICUs for 11 HCW types, collapsed into six groupings; and 2) the presence in formal ICU clinical rounds of nine HCW types, collapsed into six groupings. Bedside nurses were assumed to be involved in both categories. Analysis was descriptive, seeking to examine the predominant and full range of staffing patterns. Results Of surveyed ICUs, 94% were in metropolitan areas, 63% in teaching hospitals, 74% had >250 beds, 66% cared for mixed adult patient types (e.g., medical-surgical), median ICU bed count was 20 (interquartile range, 12–25), and 27% used some form of telemedicine. In addition to bedside nurses, the core staffing group comprised intensivists, respiratory therapists and pharmacists; in 88% of ICUs all were available to provide care. However, there were 28 different combinations of the six groupings (intensivists, respiratory therapists, pharmacists, attending physician support, advanced bedside nurse support, nurse aides), with the most common one, present in 38% of ICUs, including all six. Ninety-six percent of ICUs had interprofessional rounds at least 5 days per week; 78% had them on weekends. Among the ICUs with rounds, 61% of weekday rounding teams included all of intensivists, respiratory therapists, and pharmacists. Nutrition, rehabilitation, and social support practitioners each participated in rounds in 35–80% of ICUs and altogether in 28% of ICUs. Except for intensivists, all HCW types participated much less commonly in weekend than in weekday rounds. Conclusions ICU care almost always included a core team of bedside nurses, intensivists, respiratory therapists, and pharmacists. Beyond that core, great variability was seen in the presence of many other HCW types. Almost all ICUs had interprofessional rounds, with three-fourths also having them on weekends.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".