Protocol for an extended scoping review on the use of virtual nominal group technique in research
Bibliographic record
Abstract
INTRODUCTION: Consensus group methods such as the Nominal Group Technique (NGT) and Delphi method are commonly used in research to elicit and synthesize expert opinions when evidence is lacking. Traditionally, the NGT involves a face-to-face interaction. However, due to the COVID-19 pandemic, many in-person meetings have moved to online settings. It is unclear to what extent the NGT has been undertaken in virtual settings. The overarching aim of this scoping review is to explore the use of the virtual NGT in research. Our specific objectives are to answer the following questions: To what extent has the NGT been used virtually? What modifications were made to accommodate this online format? What advantages and disadvantages were noted by authors in comparison with the face-to-face mode of the technique? MATERIALS AND METHODS: This scoping review will follow the steps outlined by Arksey and O'Malley and the PRISMA-ScR guidelines. Several pilot searches were completed to refine inclusion and exclusion criteria. Media Synchronicity Theory will provide a conceptual framework to inform the research, including data extraction and summarizing results. As an additional extension to the literature review, online interviews with corresponding authors will be conducted to gather further information.
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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 | 0.178 | 0.239 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.142 | 0.037 |
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".