Using Zoom Videoconferencing for Qualitative Data Collection: Perceptions and Experiences of Researchers and Participants
Bibliographic record
Abstract
Advances in communication technologies offer new opportunities for the conduct of qualitative research. Among these, Zoom—an innovative videoconferencing platform—has a number of unique features that enhance its potential appeal to qualitative and mixed-methods researchers. Although studies have explored the use of information and communication technologies for conducting research, few have explored both researcher and participant perspectives on the use of web and videoconferencing platforms. Further, data are lacking on the benefits and challenges of using Zoom as a data collection method. In this study, we explore the feasibility and acceptability of using Zoom to collect qualitative interview data within a health research context in order to better understand its suitability for qualitative and mixed-methods researchers. We asked 16 practice nurses who participated in online qualitative interviews about their experiences of using Zoom and concurrently recorded researcher observations. Although several participants experienced technical difficulties, most described their interview experience as highly satisfactory and generally rated Zoom above alternative interviewing mediums such as face-to-face, telephone, and other videoconferencing services, platforms, and products. Findings suggest the viability of Zoom as a tool for collection of qualitative data because of its relative ease of use, cost-effectiveness, data management features, and security options. Further research exploring the utility of Zoom is recommended in order to critically assess and advance innovations in online methods.
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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 | 0.083 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".