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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".