Nurse Practitioner Role and Practice Environment in Primary and in Nonprimary Care in California
Bibliographic record
Abstract
Between 2008 and 2016, there was an increase in nurse practitioners in specialty care. This study explores some differences in role and practice environment between primary care and nonprimary care nurse practitioners in the domains of time spent on activities, barriers to providing care, working to scope of practice, full skill utilization, and satisfaction. This cross-sectional quantitative study, based on data from the 2017 Survey of California Nurse Practitioners and Certified Nurse Midwives, found that nurse practitioners in nonprimary care practices have lower odds of reporting time as a barrier to practice, lower odds of reporting practice to full scope, and higher odds of reporting a hierarchical or supervisory relationship with the physician. Future exploration of these differences may shed light on ways to promote nonprimary care practice environments to foster more effective collaboration and fewer barriers to providing care.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".