A framework for the introduction and evaluation of advanced practice nursing roles
Bibliographic record
Abstract
AIM: This paper describes a participatory, evidence-based, patient-focused process for advanced practice nursing (APN) role development, implementation, and evaluation (PEPPA framework). BACKGROUND: Despite the growing demand for advanced practice nurses, there are limited data to guide the successful implementation and optimal utilization of these roles. The participatory, evidence-based, patient-focused process, for guiding the development, implementation, and evaluation of advanced practice nursing (PEPPA) framework is an adaptation of two existing frameworks and is designed to overcome role implementation barriers through knowledge and understanding of APN roles and environments. The principles of participatory action research directed the construction of the new framework. CONCLUSIONS: The process for implementing and evaluating APN roles is as complex and dynamic as the roles themselves. The PEPPA framework is shaped by the underlying principles and values consistent with APN, namely, a focus on addressing patient health needs through the delivery of coordinated care and collaborative relationships among health care providers and systems. Engaging environmental stakeholders as participants in the process provides opportunity to identify the need and shared goals for a clearly defined APN role. The process promotes increased understanding of APN roles and optimal use of the broad range of APN knowledge, skills, and expertise in all role domains and scope of practice. The steps for planning and implementation are designed to create environments to support APN role development and long-term integration within health care systems. The goal-directed and outcome-based process also provides the basis for prospective ongoing evaluation and improvement of both the role and delivery of health care services.
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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 | 0.302 | 0.187 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".