Visual analysis of global comparative mapping of the practice domains of the nurse practitioner/advanced practice nursing role in respondent countries
Bibliographic record
Abstract
BACKGROUND: Internationally, there is increasing demand for nurse practitioner (NP) and advanced practice nursing (APN) roles; however, high variability exists in how NP/APN roles are defined and understood. PURPOSE: The aim of this research was to improve our understanding of how the NP/APN is defined globally by: 1) examining role definitions, competencies, and standards of practice for advanced practice nurses internationally; 2) describing from a global perspective the core concepts and common features of NP/APN associated with practice domains; and 3) exploring the utility of text mining and visual analytics in identifying the clustered core concepts common to NP/APN roles organized around the five advanced practice domains of the Strong Advanced Practice Model. METHODS: This article describes the findings of a secondary analysis of an international NP/APN competency mapping project, using innovative text mining and visual analysis techniques to reexamine and summarize the NP/APN role in 19 countries from Africa, Australia, Asia, Europe, and North America. RESULTS: Although weak aggrupation/associations suggest that further work is needed to define the domains of advanced practice with associated model development, visual analysis points to the identification of common concepts and linkages between concepts for each practice domain of advanced practice outlined in the Strong Model. IMPLICATIONS FOR PRACTICE: The secondary text mining and visual analysis presented in this article allows for comparison of core elements between advanced practice role descriptions, standards, and competencies globally to ultimately provide a global perspective on the common features of NP/APN roles and areas where further delineation is required.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".