Unlocking the potential of primary healthcare nurse practitioners globally: A concept analysis of their added value
Bibliographic record
Abstract
BACKGROUND: Primary healthcare nurse practitioners (PHCNPs) are expanding globally to meet rising healthcare demands. Despite positive outcomes, their added value remains underexplored. PURPOSE: To clarify and refine the added value PHCNPs bring. METHODS: Building on a previous concept analysis of the added value of nurse practitioners, we analyzed 37 systematic reviews focusing on PHCNPs' roles across various settings, using Rodgers' evolutionary approach. FINDINGS: Attributes include PHCNPs' skills and competencies, activities, positive outcomes, and professional autonomy. Antecedents involve PHCNP characteristics and structural and institutional factors, while consequences highlight improved patient outcomes, satisfaction, professional dynamics, and system efficiency. DISCUSSION: Understanding PHCNPs' added value is crucial for policy development, role implementation, and healthcare optimization. Clear regulatory frameworks and supportive policies are needed to maximize their impact. CONCLUSION: This analysis provides and updates the conceptual definition of PHCNPs' added value, offering insights for policy, education, and research to support their critical role in healthcare.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".