Using Zoom Annotate to Facilitate Online Focus Groups in Veterinary Medicine Education Research
Bibliographic record
Abstract
Focus groups allow researchers to collect data from multiple participants on a set of questions while simultaneously observing participant interactions during sessions. Traditionally, researchers conduct focus groups in person, though online focus groups have been increasingly used as technologies have improved. The pandemic increased the need for researchers to innovate online focus group practices. This paper aims to present best practices for using annotation functions on digital video conference platforms to conduct focus group interviews in veterinary medicine education research. We explain how Zoom, specifically its Annotate functions, offers a useful tool to facilitate online focus groups and assist veterinary medicine education research and practice. This method addresses many of the challenges that in-person focus groups have-dominant participants, geographical barriers, and confidential (instead of anonymous) participation-while still being able to collect quality data during a group interview. The best practices described here allow for capturing both qualitative and quantitative data from online participants while preserving their anonymity and increasing the ease of participation. Based on data we have collected, participants report being comfortable providing honest and direct responses across a variety of questions. This practice also allows the collection of simultaneous or delayed answers, which means that participants have more flexibility in how and when they respond compared to many in-person focus groups. This practical approach to online focus group research can assist in conducting veterinary medicine education research not just during the pandemic but whenever geographical barriers or a need for increased confidentiality are researcher concerns.
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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 | 0.056 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".