Virtual Synchronous Qualitative Data Collection Methods Used in Health and Social Sciences: A Scoping Review of Benefits, Challenges and Practical Insights
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, we have seen the use of virtual synchronous qualitative data-collection methods grow exponentially, especially within the context of the COVID-19 pandemic. Although several recommendations for researchers conducting in-person interviews and focus groups are available in the scientific literature, they are not necessarily suited for application in a virtual context. To gain a better understanding of current practices and recommendations in virtual synchronous qualitative data collection, we conducted a scoping review. Information obtained from the 70 articles included in this review highlights the main benefits and challenges of virtual data-collection methods in research, compares differences with in-person means of data collection, and provides readers with practical insights for before, during, and after data collection. This comprehensive overview of the existing literature allowed us to outline the theoretical contributions and practical implications of our work as well as provide perspectives for future research. This scoping review can serve as a tool to inform researchers about how best to conduct virtual synchronous qualitative data collection based on other researchers’ prior experiences and the recommendations currently available in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.183 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it