International overview of the advanced practice nurse training: scoping review
Bibliographic record
Abstract
Abstract Objective To map, in the international literature, models of advanced practice nursing education in different countries around the world. Methods This is a scoping review conducted according to the Joanna Briggs Institute (JBI) methodology and reported according to the PRISMA-ScR guideline. The research question was structured using the PCC strategy (Population: nurses; Concept: advanced practice education; Context: various countries). Studies published between 2019 and 2025 in English, Portuguese, and Spanish were included, with searches in four databases: PubMed, Web of Science, LILACS, and SciELO, as well as gray literature. Results Nineteen studies were included, mostly from English-speaking countries. There was great diversity in the educational levels required, curriculum content, and pedagogical strategies. Countries such as the USA, the United Kingdom, and Canada had consolidated training programs with a high degree of professional autonomy, while Latin American and African countries still face regulatory, structural, and pedagogical challenges. Conclusion The training of advanced practice nurses presents heterogeneous models around the world, requiring the formulation of flexible global guidelines that ensure the quality of training, respect local contexts, and promote mobility and professional recognition. Registration https://doi.org/10.17605/OSF.IO/DJHGE
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.045 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".