Expanding Qualitative Research Interviewing Strategies: Zoom Video Communications
Bibliographic record
Abstract
The proliferation of new video conferencing tools offers unique data generation opportunities for qualitative researchers. While in-person interviews were the mainstay of data generation in qualitative studies, video conferencing programs, such as Zoom Video Communications Inc. (Zoom), provide researchers with a cost-effective and convenient alternative to in-person interviews. The uses and advantages of face-to-face interviewing are well documented; however, utilizing video conferencing as a method of data generation has not been well examined. The purpose of this paper is to examine the specific attributes of Zoom that contribute to high quality and in-depth qualitative interviews when in person interviewing is not feasible. While video conferencing was developed to facilitate long-distance or international communication, enhance collaborations and reduce travel costs for business these same features can be extended to qualitative research interviews. Overall, participants reported that Zoom video conferencing was a positive experience. They identified strengths of this approach such as: (1) convenience and ease of use, (2) enhanced personal interface to discuss personal topics (e.g., parenting), (3) accessibility (i.e., phone, tablet, and computer), (4) time-saving with no travel requirements to participate in the research and therefore more time available for their family. Video conferencing software economically supports research aimed at large numbers of participants and diverse and geographically dispersed populations.
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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 | 0.084 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".