Provider to Patient Ratios for Nurse Practitioners and Physician Assistants in Critical Care Units
Bibliographic record
Abstract
BACKGROUND: Nurse practitioners and physician assistants are being increasingly integrated into intensive care unit and hospital-based care teams, yet limited information is available on provider to patient ratios. OBJECTIVE: To determine current provider to patient ratios for nurse practitioners and physician assistants working in intensive and acute care units and to assess factors that affect the ratios. METHODS: A descriptive study design was used with a Web-based survey of members of the American Association of Nurse Practitioners, American Academy of Physician Assistants, and the Society of Critical Care Medicine. RESULTS: Responses were received from 222 nurse practitioners and 211 physician assistants from all but 8 of the 50 United States and from Canada. Mean provider to patient ratios in intensive care were 1 to 5 (range, 1 to 3 - 1 to 8). In pediatric intensive care, the mean ratio of nurse practitioners to patients was 1 to 4 (range, 1 to 3 - 1 to 8). Factors that affected nurse practitioner and physician assistant provider to patient ratios included patients' severity of illness, number of patients in the unit, number of providers in the unit, patient diagnosis, number of physicians in the unit, time of day, and number of fellows and medical residents on service. CONCLUSIONS: Additional information on factors influencing provider to patient ratios and specific components of the roles of nurse practitioners and physician assistants will be important to ensure the best utilization of these providers to enable optimal patient care 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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".