Instruments for measuring nursing research competence: a protocol for a scoping review
Bibliographic record
Abstract
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.
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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 | 0.199 | 0.199 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.078 | 0.023 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".