Hospital‐based nurse practitioner roles and interprofessional practice: A scoping review
Bibliographic record
Abstract
This scoping review provides current global understanding of the rapidly evolving nurse practitioner role within hospital settings, and considers the level of understanding of its enactment within interprofessional teamwork. Arksey and O'Malley's framework was used to explore recent primary research, reviews, and gray literature in two ways. First, hospital-based nurse practitioner literature was mapped to country of origin, and thematically summarized. Second, clearly developed and consistently defined key interprofessional concepts were identified in the interprofessional literature then conceptually mapped to the nurse practitioner studies by their operationalization. The nurse practitioner review located 103 abstracts. Twenty-nine, originating from four countries, met the inclusion criteria. The interprofessional concept review identified a total of 137 relevant abstracts, however, only ten met the inclusion criteria. Understanding the nurse practitioner role within hospital teams remains limited due to a small number of countries producing evidence, the lack of nurse practitioner role title standardization hindering consistent knowledgebase development, and limited application and inconsistent operationalization of concepts within nurse practitioner research. Research focused on role enactment is needed to understand the uniqueness of the hospital-based nurse practitioner role.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| 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".