Recommendations for Researchers on Synchronous, Online, Nominal Group Sessions in Times of COVID-19: Fishbone Analysis
Bibliographic record
Abstract
BACKGROUND: In times of COVID-19, we are challenged to experiment with alternative platforms or software to connect people. In particular, the struggle that arose in health research was how to interact with patients and care professionals. The latter is additionally faced with an extreme workload to fight the pandemic crisis. Creative strategies have been developed to continue research among patients and care professionals to improve quality of care. This paper addresses the issue of synchronous, online, nominal group sessions, a common consensus method used for group brainstorming. OBJECTIVE: The purpose of this study was to share our experiences with performing online, nominal group sessions using the video conference software Microsoft Teams. In addition, we aimed to create a practical guide with recommendations for researchers. METHODS: We critically analyzed the procedures for the online nominal group technique, according to the Fishbone methodology. RESULTS: Performing synchronous, online, nominal group sessions is challenging but offers opportunities. Although interaction with and among the attendees complicates the process, the major advantage of online sessions is their accessibility and comfort because of reduced barriers to participation (eg, lower time investment). The role of the moderators is of major importance, and good preparation beforehand is required. Recommendations for future online, nominal research were formulated. CONCLUSIONS: Online, nominal group sessions seem to be a promising alternative for the real-life commonly used technique. Especially during the COVID-19 pandemic, the benefits must be highlighted. More expertise is needed to further refine the practical guide for using digital software in research and to achieve optimal performance.
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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 | 0.357 | 0.621 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.014 | 0.014 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.033 | 0.020 |
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".