The use of virtual nominal groups in healthcare research: An extended scoping review
Bibliographic record
Abstract
INTRODUCTION: The Nominal Group Technique (NGT) is a consensus group method used to synthesize expert opinions. Given the global shift to virtual meetings, the extent to which researchers leveraged virtual platforms is unclear. This scoping review explores the use of the vNGT in healthcare research during the COVID-19 pandemic. METHODS: Following the Arksey and O'Malley's framework, eight cross-disciplinary databases were searched (January 2020-July 2022). Research articles that reported all four vNGT stages (idea generation, round robin sharing, clarification, voting) were included. Media Synchronicity Theory informed analysis. Corresponding authors were surveyed for additional information. RESULTS: Of 2,589 citations, 32 references were included. Articles covered healthcare (27/32) and healthcare education (4/32). Platforms used most were Zoom, MS Teams and GoTo but was not reported in 44% of studies. Only 22% commented on the benefits/challenges of moving the NGT virtually. Among authors who responded to our survey (16/32), 80% felt that the vNGT was comparable or superior. CONCLUSIONS: The vNGT provides several advantages such as the inclusion of geographically dispersed participants, scheduling flexibility and cost savings. It is a promising alternative to the traditional in-person meeting, but researchers should carefully describe modifications, potential limitations, and impact on results.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".