Development of a Taxonomy of Policy Interventions for Integrating Nurse Practitioners into Health Systems
Bibliographic record
Abstract
BACKGROUND: Nurse practitioners (NPs)-nurses with advanced training who can provide and prescribe care-are increasingly prevalent internationally. The growth of NPs can be attributed to physician shortages, growing demand for health services, and the professionalization of nursing. Ensuring efficacious integration of NPs into the health system is critical for maximizing their impact on patient and system outcomes. Nonetheless, there is a dearth of information on practical policy interventions facilitating the successful integration of NPs. Effective policy interventions should capture the perspectives of actors at all levels of the health system-ie, national and organizational levels. This study aimed to delineate a taxonomy of policy interventions for integrating NPs into health systems. This paper presents a taxonomy developed among gerontology NPs in Israel. METHODS: This qualitative descriptive study used multiple perspective, one-to-one interviews with four professional groups-national policy-makers, organizational administrators, NPs, and physicians. Data were analyzed using deductive content analysis. Analysis accounted for diverging and converging patterns between professional groups. RESULTS: There were 58 participants across the four professional groups. The national-level domain interventions include marketing, workforce development, professional licensure and regulation, financial incentives, stakeholder cooperation, education and training programs, and national-level research. Organizational domain interventions included organizational guidelines, infrastructure development and resource allocation, interprofessional leadership engagement, and organizational messaging. Unit- and care-team domain interventions included interprofessional experience and exposure, team communication, and mentorship. CONCLUSION: The taxonomy's trichotomy of three health system level domains describes the relationship between national and organizational policy interventions. Adopting these interventions may result in an improved response to provider shortages. Policies insufficiently addressing role clarity and workforce retention resulted in poor integration and a failure to efficaciously combat workforce shortages. Future work will expand the focus of this preliminary taxonomy by further development and testing with international participants.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".