Availability of Advanced Practice Providers in Adult Intensive Care Units in the United States: A Survey
Bibliographic record
Abstract
BACKGROUND: How advanced practice providers (APPs) are deployed in adult US intensive care units (ICUs) is understudied. Further, whether state-level restrictions on practice affect the availability of these providers is unknown. OBJECTIVES: To describe staffing patterns of ICU APPs (nurse practitioners, physician assistants) in the context of physicians-in-training (interns, residents, fellows) and to explore the association between state-level APP practice restrictions and employment. METHODS: Data from a national survey of pre-COVID-19 (steady-state) ICU staffing linked to the 2020 American Hospital Association survey were used to examine staffing patterns (via descriptive statistics) and to explore the association of state-level practice restrictions with the presence of APPs in ICUs (via multivariable regression). RESULTS: The cohort included 588 adult ICUs, of which 336 (57.1%) reported both APPs and physicians-in-training, 124 (21.1%) APPs only, 73 (12.4%) physicians-in-training only, and 55 (9.4%) neither. Units with both provider types were more commonly surgical ICUs (17.6% vs ≤9.6%; P < .001), whereas those with neither were 98.2% mixed units. Those units with neither were smaller and more often in smaller, nonteaching, for-profit hospitals in nonmetropolitan areas. Two hundred twenty-five ICUs (38.3%) were in states allowing full APP practice scope. After adjustment, the odds of employing APPs were nonsignificantly higher in ICUs in full-practice states. CONCLUSIONS: Both APPs and physicians-in-training are commonly deployed in US adult ICUs, often together. Laws limiting practice scope may impede deployment of these providers in ICUs.
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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 | 0.002 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".