Challenges in Conducting Online Videoconferencing Qualitative Interviews with Adolescents on Sensitive Topics
Bibliographic record
Abstract
In the wake of COVID-19, researchers are seeking innovative data-collection methods. Computer-mediated communication platforms have played a pivotal role among these pursuits. However, conducting online interviews present challenges to both researchers and participants. Online data-collection forces researchers to give up control over the study environment due to the varying location participants partake in interviews. Consequently, researchers can no longer fully guarantee the confidentiality and privacy of the researcher-participant conversations. Participants may face difficulties if being asked to disclose private information in the presence of family members. These challenges are heightened when conducting online interviews with adolescents on sensitive topics. Thus, attention to the rigour of qualitative research is a fundamental consideration given these limitations in technical and social conventions with the use of online data-collection methods. Despite the host of challenges, online interviewing creates valuable opportunities for researchers to rise to the challenge of social distancing in their data-collection efforts.
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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 | 0.470 | 0.407 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".