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Record W4399401180 · doi:10.1515/ijnes-2023-0058

Formal nursing focused academic practice partnerships for advancing nursing research and scholarship: a scoping review protocol

2024· review· en· W4399401180 on OpenAlexaffabout
Sandra Filice, Sharon Broughton, Lisa Giallonardo, Sawith Abeygunawardena, Rebecca Pereira

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2024
Typereview
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsScholarshipNursing researchNursingNursing practiceProtocol (science)MedicinePolitical scienceAlternative medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: This scoping review protocol will be used to map the evidence regarding structure and organization of formal nursing undergraduate focused academic practice partnerships in Canada and globally. DESIGN: This scoping review will adhere to guidance provided by Chapter 11 of the JBI Manual for Evidence Synthesis: Scoping Reviews guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Review extension checklist. METHODS: Evidence will be eligible for inclusion if published in English, within the last 10 years, and available in full text. Databases will be searched for published literature and unpublished grey literature. DISCUSSION: This protocol provides guidance on conducting a scoping review on formal nursing undergraduate focused academic practice partnerships. The review will enhance understanding of the structure and organization of formal nursing undergraduate focused academic practice partnerships, informing the design and work of future partnerships. This protocol is registered in the Open Science Framework https://doi.org/10.17605/OSF.IO/JCTRM.

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 imitation

Not 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.

metaresearch head score (Codex)0.195
metaresearch head score (Gemma)0.182
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.195
Threshold uncertainty score0.992

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1950.182
Meta-epidemiology (narrow)0.0040.006
Meta-epidemiology (broad)0.0080.009
Bibliometrics0.0180.015
Science and technology studies0.0070.007
Scholarly communication0.0090.013
Open science0.0060.011
Research integrity0.0120.012
Insufficient payload (model declined to judge)0.1140.044

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 head0.380
GPT teacher head0.630
Teacher spread0.250 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
Domainnot available
GenreProtocol

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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