Role of Advanced Nurse Practitioners and Physician Assistants in Washington State
Bibliographic record
Abstract
PURPOSE: In response to the looming oncology manpower shortage, the Washington State Medical Oncology Society (Vancouver, WA) surveyed medical oncology practices in Washington regarding employment patterns for advanced nurse practitioners (ANPs) and physician assistants (PAs) in oncology. METHODS: Funded by a 2008 ASCO State Affiliate Grant and using Surveymonkey.com as a platform, practices were queried regarding practice demographics, employment patterns, and functions of ANPs and PAs. RESULTS: Of the total queried, 25 practices (50%) responded, representing the spectrum of single-specialty (n = 8), multispecialty (n = 7), hospital-based (n = 6), and academic practices (n = 4). Sixty-eight percent of practices employed ANPs and/or PAs. Compared with PAs, ANPs were more likely to work independent of supervision (64% v 0%), perform follow-up in the outpatient setting (93% v 77%), evaluate patients in infusion centers (71% v 62%), and provide genetic counseling (42% v 0%). PAs were more likely to see hospitalized patients (62% v 42%) and supervise infusion centers (15% v 7%). New patient consultations were performed with similar frequency (PAs, 29% and ANPs, 31%). ANPs were more likely to review research eligibility (25% v 15%), obtain informed consent (33% v 15%), and monitor compliance (13% v 8%). CONCLUSION: Washington practices have embraced advanced practitioners. Given the diversity of practice patterns, practices can learn from one another how to maximize ANP/NP roles. Practices need to promote practice-based educational opportunities to attract ANPs/PAs to medical oncology.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".