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Record W4385799715 · doi:10.1097/cin.0000000000001061

Nurse Practitioner Regulatory Assessment

2023· review· en· W4385799715 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCIN Computers Informatics Nursing · 2023
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsUsabilityDocumentationWorkflowQuality assuranceNursingQuality (philosophy)Medical educationMedicineBusinessComputer scienceMarketing

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.472
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it