Zoom Interviews: Benefits and Concessions
Bibliographic record
Abstract
COVID-19 restrictions have transitioned in-person qualitative research interviews to virtual platforms. The purpose of the current article is to detail some benefits and concessions derived from our experiences of using Zoom to interview men about their intimate partner relationship breakdowns and service providers who work with men to build better relationships. Three benefits; 1) Rich therapeutic value, 2) There’s no place like home, and 3) Reduced costs to extend recruitment reach and inclusivity, highlighted Zoom’s salutary value, the data richness afforded by being interviewed from home, and the potential for cost-effectively progressing qualitative study designs. In particular, reduced labour and travel costs made viable wider reaching participant recruitment and multi-site data collection. The concessions; 1) Being there differently, 2) Choppy purviews and 3) Preparing and pacing, and adjusting to the self-stream revealed the need for interviewers to nimbly adjust to circumstances outside their direct control. Included were inherent challenges for adapting to diverse interviewee locations, technology limits and discordant audio-visual feeds. Amongst these concessions there was resignation that many in-person interview nuances were lost amid the virtual platform demanding unique interviewer skills to compensate some of those changes. Zoom interviews will undoubtedly continue post COVID-19 and attention should be paid to emergent ethical and operational issues.
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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 | 0.169 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".