Targeting the nurse practitioner workforce: Influences and barriers in choosing rural practice
Bibliographic record
Abstract
Background and objective: Recruitment and retention of primary care providers are projected to worsen in rural regions. Nurse practitioners (NPs) are a crucial solution to the shortage of primary care providers in rural America. Little research exists regarding factors influencing new NPs’ decisions to practice in rural settings, as well as practice readiness. The purpose of this study is to explore factors influencing new NPs’ decision to practice in rural settings.Methods: A survey of family nurse practitioner (FNP) graduates in a rural state was conducted. The survey measured rural background, current practice environment, the impact of rural clinical experiences on readiness to practice, and perceptions of rural NP practice.Results: The data collected over five years (N = 42) indicated several factors that influenced an NP’s decision to choose a position in a rural or underserved setting. A wide scope of practice, rural roots, a desirable job offer, and strong relationships were influential when choosing rural practice.Conclusions: Most respondents (69%) were not practicing in rural or underserved areas. Among those who were, the ability to practice to the full scope of education and autonomy were the most important factors. However, respondents were also apprehensive and intimidated with the broad skill set required in rural care. Implications: This study provides insight into factors and barriers for new graduate NPs in choosing a rural practice setting as well as possible solutions to the rural workforce shortage.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".