Development and implementation of the specialized nurse practitioner role: Use of the PEPPA framework to achieve success
Bibliographic record
Abstract
In 2003, amendments to the Nurses' Act in Quebec, Canada, allowed for an expanded nursing role. Specialized nurse practitioners were introduced to the health-care system in 2005. By merging medical knowledge with advanced practice nursing, the specialized nurse practitioner is gaining in popularity and acceptance with staff members and patients. To guide our team through the process, we used the PEPPA (participatory, evidence-based, patient-focused process for guiding the development, implementation, and evaluation of advanced practice nursing) framework. By using a framework specifically designed for the development, implementation, and evaluation of an advanced practice nursing role, we were better prepared for the path that lay ahead. Ultimately, the goal of the implementation of the specialized nurse practitioner role is to improve the quality of care to a specific population of patients, whether it is through individualized clinical follow-up, evidence-based practice, patient teaching, or promoting continuous education for the nurses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.166 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".