Using the PEPPA Framework to Develop and Implement a Nurse Practitioner Role Within Canada’s National Ballet School
Bibliographic record
Abstract
Aim: To outline the successful development and implementation of a nurse practitioner role within a professional ballet school. Background: Nurse practitioners are well integrated into primary and acute care in Ontario, yet the role within schools and private athletic institutions is not well documented. Canada’s National Ballet School is a professional ballet school with a combination of day students and those living in residences. Students complete both dance training and academics at the School. The physical and mental health of students was identified as a key priority by the school, leading to the development of an integrated health and wellness program. To facilitate more timely access to healthcare and provide an opportunity for collaboration and consultation within the school, a plan to implement a nurse practitioner role into the school was developed. Methods: In order to develop and implement the role of the nurse practitioner within the institution, the participatory, evidence-based, patient-focused process for advanced practice nursing role development, implementation, and evaluation (PEPPA) framework was used. The first seven steps of the PEPPA framework were applied in this project. Findings: The PEPPA framework allowed for us to identify key barriers and facilitators for the role implementation and successfully implement the nurse practitioner role. While the initial plan was for a slower implementation, the COVID-19 pandemic highlighted the need for a nurse practitioner in the institution more urgently. Conclusion: The PEPPA framework provided us with an organized process for developing and implementing the nurse practitioner role at Canada’s National Ballet School.
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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 | 0.135 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| 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".