Nurse practitioner integration: Conceptual development to enhance application in policy and research
Bibliographic record
Abstract
BACKGROUND: Nurse practitioners (NPs) have been introduced across the world to improve care quality and solve provider shortages. Realizing these benefits relies on their successful integration into health care systems. Although NP integration has been discussed extensively, the concept is defined inconsistently. Literature, therefore, cannot be synthesized to create policy recommendations for management and policymakers to plan for and advance NP integration. OBJECTIVES: To describe and define NP integration and enhance its applicability in research and policy. DATA SOURCES: A modified Walker and Avant concept analysis was used to develop a conceptual model of NP integration. Data were extracted and synthesized from 78 sources referencing the concept. CONCLUSIONS: Nurse practitioner integration was operationally defined as the multilevel process of incorporating NPs into the health care system so that NPs can practice to their full scope, education, and training and contribute to patient, system, and population needs. The attributes of NP integration are: 1) achievable goal; 2) process; 3) introduction of the role; 4) incorporation into organizational care models; 5) challenging traditional ideologies; 6) ability to function; 7) provide high-quality care; and 8) improve outcomes, sustainability, and health system transformation. Seventeen facilitators/barriers affecting NP integration were identified. Three health care system levels at which integration occurs were identified- macro , meso , and micro . IMPLICATIONS FOR PRACTICE: Findings will inform managers, policymakers, and stakeholders about NP integration to aid in planning and policy development. Results can be used to inform research on barriers and facilitators to NP integration.
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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 | 0.106 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".