Challenges in Conducting Online Videoconferencing Qualitative Interviews with Adolescents on Sensitive Topics
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 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.
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 | 0.019 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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