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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".