Advanced Practice Nursing Roles, Regulation, Education, and Practice: A Global Study
Bibliographic record
Abstract
Background and Objectives: Several subgroups of the International Council of Nurses Nurse Practitioner/Advanced Practice Nurse Network (ICN NP/APNN) have periodically analyzed APN (nurse practitioner and clinical nurse specialist) development around the world. The primary objective of this study was to describe the global status of APN practice regarding scope of practice, education, regulation, and practice climate. An additional objective was to look for gaps in these same areas of role development in order to recommend future initiatives. Methods: Annual ICN NP/APNN Conference in Rotterdam, Netherlands. Links to the survey were provided there and via multiple platforms over the next year. Results: Survey results from 325 respondents, representing 26 countries, were analyzed through descriptive techniques. Although progress was reported, particularly in education, results indicated the APN profession around the world continues to struggle over titling, title protection, regulation development, credentialing, and barriers to practice. Conclusions and Practice/Policy Relevance: APNs have the potential to help the world reach the Sustainable Development Goal of universal health coverage. Several recommendations are provided to help ensure APNs achieve these goals.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".