International Comparison of Professional Competency Frameworks for Advanced Practice Nurses
Bibliographic record
Abstract
Abstract Background Advanced Practice Nurses (APNs) have become established professionals in many healthcare systems. APNs combine nursing with medical expertise, bridging the boundaries of traditional professions in nursing and medicine. This study focuses on the content of professional competency frameworks for APNs in different countries to determine (1) how these frameworks define the competencies of APNs and (2) address the division of tasks between APNs and physicians. Methods An inductive content analysis is conducted of the competency frameworks for APNs in Belgium, Canada, the Netherlands, the United Kingdom, and the United States. Results In Belgium, the Netherlands and the US, the framework is based on an existing model, while in Canada and UK, frameworks do not refer to a model. The analysis revealed twelve generic competency domains for APNs: 1. Clinical Care in Practice, 2. Independent Practitioner, 3. Professional Attitude, 4. Scientific Research & Evidence-Based Practice Care, 5. Leadership, 6. Health Promotion and Public Healthcare, 7. Communication and Collaboration, 8. Organisation of Care and Policy, 9. Support of Self-Management and Patient Empowerment, 10. Quality and Safety of Care, 11. Teaching and (continuing) Education, 12. Technology and E-Health. There are some differences in explicitly to which high-risk procedures are described per country. The frameworks provide limited descriptions of task division and delegation from physicians to APNs, except for the frameworks from Belgium and the Netherlands. Conclusions Despite variations in the basis of professional competency frameworks per country and country-specific influences, twelve generic competency domains with similar competencies were identified. It is recommended to stimulate the development of a global standardised competency framework for APNs, including clear descriptions of task division and delegation, to optimise their role in delivering high-quality healthcare services. Key messages • Similar APN competency expectations suggest the need for an international competency framework, including roles and tasks for APNs, to function as a basis for individual country frameworks. • Nursing associations can use this study to learn from other countries and enhance their development of country-specific competency frameworks for advanced practice nurses.
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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 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".