An integrative review of new nurse practitionersâ experiences in rural healthcare practice
Bibliographic record
Abstract
INTRODUCTION: A maldistributed primary care workforce and disparities in health outcomes are ongoing concerns for rural populations across the globe. Nurse practitioners (NPs) are a promising solution for mitigating rural healthcare inequities by reducing provider shortages and improving access to essential primary care services. The NP workforce is the fastest growing sector of primary care providers in the US. NPs are more likely than their physician colleagues to spend careers in rural and underserved settings practicing in isolation from other providers, with higher rates of turnover. An indistinct understanding of rural NPs' early career experiences highlights the need for a critical synthesis of the literature and key future recommendations. This integrative review aimed to analyze and synthesize various types of empirical reports and theoretical articles about new NPs' experiences in rural primary healthcare practice; identify current literature gaps; and discuss implications for education, policy, and further research. METHODS: Whittemore and Knafl's integrative method was used to inform the selection, review, and analysis of the literature. Search keywords were based on the Population, Effect of Interest, Measure, Study Design, Setting framework: (1) population (primary care NPs), (2) effect of interest (early career phase in a rural context), (3) measure (NP perspectives about their experiences), (4) study design (empirical, theoretical), and (5) setting (rural US and countries with a similar healthcare system and NP workforce, such as Australia, Canada, Ireland, Netherlands, and New Zealand). Four key databases (PubMed, Embase, Web of Science and CINAHL) were searched, followed by manual searching of reference lists to identify relevant empirical and theoretical literature; no time delimitation was applied in the search. A total of 174 sources were scanned. Data were iteratively compared, and significant patterns were extracted and organized into thematic clusters. RESULTS: The literature search yielded five studies that met the eligibility criteria: three phenomenological studies, one descriptive qualitative study, and one descriptive quantitative study. Three themes emerged: the trajectory of early career practice for rural NPs, commitment and persistence of new rural NPs, and adaptive and maladaptive early career factors for rural NPs. CONCLUSION: This review included articles published in the US, although emergent themes may contribute to global knowledge about early career experiences in rural settings where advanced practice nurses are used. This review reinforced that NPs as a distinct professional population are underrepresented in rural workforce research, particularly during their early career phases. Scholarly literature about new rural NPs emphasized clinical preparedness and competence, workplace recruitment incentives, transition-to-practice experiences, and the importance of mentoring and professional networks. However, findings are limited primarily to the first year of practice. Future research priorities include exploring the ways to support rural NPs' wellbeing during the transition-to-practice phase, the barriers and facilitators to their job satisfaction by career stage, and factors contributing to burnout and turnover. Further exploration of community contexts and adaptive processes are indicated to inform meaningful NP educational refinements and effective retention policies. Understanding the experiences of rural NPs who are newcomers to rural life should also be explored.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".