Considerations for conducting online focus groups on sensitive topics
Bibliographic record
Abstract
In response to concerns about the use of online focus groups, particularly around sensitive topics research, we provide two case examples of sensitive topics research that pivoted to online focus groups amid university ethics restrictions due to COVID-19 concerns. We begin by contextualizing the studies, one of which used the more traditional focus group method while the other employed a mix of focus groups and a variation on the World Café method, termed Community Cafés. We discuss issues like online platform choice (Microsoft Teams versus Zoom), security, and considerations for effective participant communication and connection. We demonstrate the effectiveness of online focus group data collection for sensitive research in two disciplines as well as the benefits to participants. We conclude by providing considerations and recommendations based on our own learnings for researchers wanting to conduct online focus group research on sensitive topics.
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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 | 0.515 | 0.555 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".