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Record W3132732730 · doi:10.1136/bmjopen-2020-042325

Instruments for measuring nursing research competence: a protocol for a scoping review

2021· review· en· W3132732730 on OpenAlexaff
Qirong Chen, Chongmei Huang, Aimee R. Castro, Siyuan Tang

Bibliographic record

VenueBMJ Open · 2021
Typereview
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineCompetence (human resources)Protocol (science)NursingNursing researchMedical educationAlternative medicinePathologyManagement

Abstract

fetched live from OpenAlex

INTRODUCTION: Nursing research competence of nursing personnel has received much attention in recent years, as nursing has developed as both an independent academic discipline and an evidence-based practiing profession. Instruments for appraising nursing research competence are important, as they can be used to assess nursing research competence of the target population, showing changes of this variable over time and measuring the effectiveness of interventions for improving nursing research competence. There is a need to map the current state of the science of the instruments for nursing research competence, and to identify well validated and reliable instruments. This paper describes a protocol for a scoping review to identify, evaluate, compare and summarise the instruments designed to measure nursing research competence. METHODS AND ANALYSIS: 's additional recommendations for applying this framework. The scoping review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. The protocol is registered through the Open Science Framework (https://osf.io/ksh43/). Eight English databases and two Chinese databases will be searched between 1 December 2020 and 31 December 2020 to retrieve manuscripts which include instrument(s) of nursing research competence. The literature screening and data extraction will be conducted by two researchers, independently. A third researcher will be involved when consensus is needed. The COnsensus-based Standards for the selection of health Measurement INstruments methodology will be used to evaluate the methodological quality of the included studies on measurement properties of the instruments, as well as the quality of all the instruments identified. ETHICS AND DISSEMINATION: Ethical approval is not needed. We will disseminate the findings through a conference focusing on nursing research competence and publication of the results in a peer-reviewed journal.

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)0.199
metaresearch head score (Gemma)0.199
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.801
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1990.199
Meta-epidemiology (narrow)0.0060.006
Meta-epidemiology (broad)0.0110.013
Bibliometrics0.0220.023
Science and technology studies0.0060.007
Scholarly communication0.0090.010
Open science0.0060.009
Research integrity0.0110.013
Insufficient payload (model declined to judge)0.0780.023

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 head0.935
GPT teacher head0.814
Teacher spread0.121 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
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

Citations20
Published2021
Admission routes1
Has abstractyes

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