Framework for Evaluating the Impact of Advanced Practice Nursing Roles
Bibliographic record
Abstract
PURPOSE: To address the gap in evidence-based information required to support the development of advanced practice nursing (APN) roles in Switzerland, stakeholders identified the need for guidance to generate strategic evaluation data. This article describes an evaluation framework developed to inform decisions about the effective utilization of APN roles across the country. APPROACH: A participatory approach was used by an international group of stakeholders. Published literature and an evidenced-based framework for introducing APN roles were analyzed and applied to define the purpose, target audiences, and essential elements of the evaluation framework. Through subsequent meetings and review by an expert panel, the framework was developed and refined. FINDINGS: A framework to evaluate different types of APN roles as they evolve to meet dynamic population health, practice setting, and health system needs was created. It includes a matrix of key concepts to guide evaluations across three stages of APN role development: introduction, implementation, and long-term sustainability. For each stage, evaluation objectives and questions examining APN role structures, processes, and outcomes from different perspectives (e.g., patients, providers, managers, policy-makers) were identified. CONCLUSIONS: A practical, robust framework based on well-established evaluation concepts and current understanding of APN roles can be used to conduct systematic evaluations. CLINICAL RELEVANCE: The evaluation framework is sufficiently generic to allow application in developed countries globally, both for evaluation as well as research purposes.
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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 | 0.257 | 0.195 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.019 | 0.009 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".