The Use of Videoconferencing as a Medium for the Qualitative Interview
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
Qualitative data collection, especially conducting in-person interviews, presents challenges for researchers whose participants are geographically dispersed. Often alternative means of interviewing using communication technology are necessary. This was true for this focused ethnographic research exploring the experiences of participants who were connected to a particular cultural group by virtue of their similar experience but who were not located in the same geographical area. The purpose of this paper is to present the experience of using videoconferencing technology to collect experiential data from undergraduate nursing students and preceptors who were dispersed over a 640,000 square kilometer area in western and northern Canada during a rural hospital-based preceptorship. Recommendations for using videoconferencing as a medium for conducting in-depth qualitative interviews include using a high-bandwidth connection such as SuperNet or Web conferencing, and evaluating whether the type of information sought is likely to be shared in other than in-person face-to-face situations.
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 | 0.088 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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