Nurse Practitioner Regulatory Assessment
Bibliographic record
Abstract
The Nurse Practitioner Onsite Peer Review is an integral part of the British Columbia College of Nurses and Midwives Quality Assurance program. Traditionally an in-person assessment, Nurse Practitioner Onsite Peer Review involves a critical review of documentation by an experienced nurse practitioner assessor against regulatory standards and entry-level competencies. The onset of the COVID-19 pandemic and resulting environmental limitations required the college to rethink its approach to onsite reviews, resulting in the quality assurance program embarking on a pilot project to explore the feasibility of conducting reviews virtually. As there are many factors that can affect the transition of an onsite assessment to one that is virtual, it was important to consider the technical, workflow, and usability aspects in developing this new method of performance assessment. Therefore, including usability testing and a human factors approach to exploring this emerging method was vital to ensuring its success. In this article, we discuss our experience, including benefits, technical and administrative considerations, barriers, challenges, and lessons learned.
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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 | 0.068 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".