California Nurse Practitioners Are Positioned To Fill The Primary Care Gap, But They Face Barriers To Practice
Bibliographic record
Abstract
Nurse practitioners are well prepared to help fill care gaps arising from shortages of primary care physicians in California. This article reports findings from a survey of California nurse practitioners that examined their employment and practice barriers. The number of nurse practitioners per capita varies across California counties and is positively correlated with the number of physicians per capita. Hispanic and Filipino nurse practitioners are more likely to live in underserved areas. Nurse practitioners and their education programs are concentrated in the same counties that have high physician-to-population ratios. In these counties, recently graduated nurse practitioners are more likely to report that they plan to relocate to another state in the next five years. Expanding education programs in underserved areas, increasing the diversity of the nurse practitioner workforce, and ensuring that nurse practitioners feel empowered to fully use their skills are necessary to meet both current and future primary care needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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".