University Health Network Framework for Advanced Nursing Practice: Development of a Comprehensive Conceptual Framework Describing the Multidimensional Contributions of Advanced Practice Nurses
Bibliographic record
Abstract
BACKGROUND: The global paradigm shift resulting from radical transformations in knowledge and technology is significantly changing the context of healthcare delivery. In this changing environment, the contributions of health professions are vital in ensuring that the healthcare system adapts to meet the needs of today's patient. Advanced practice nurses (APNs) are clinical scholars and leaders in creating innovative approaches to patient care and organizational and professional leadership. AIMS: To develop a comprehensive conceptual framework for advanced nursing practice at University Health Network that will enhance role clarity by describing the complexity of these nursing roles and the significant contributions they make to patients and the healthcare system. METHODS: A critical review of the literature and a consultative process were undertaken to build consensus and develop a comprehensive framework for advanced nursing practice. RESULTS: The development of the University Health Network Framework for Advanced Nursing Practice (UHN-FANP), which clearly articulates all dimensions of advanced nursing practice roles. CONCLUSION: As clinical leadership roles in nursing continue to evolve, utilization of a conceptual framework facilitates role clarity, role implementation and role evaluation.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| 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".