Five tips for conducting remote qualitative data collection in COVID times: theoretical and pragmatic considerations
Bibliographic record
Abstract
OBJECTIVE: To provide five methodological and pragmatic tips for conducting remote qualitative data collection during the context of the COVID-19 pandemic. METHOD: The tips presented in this article are drawn from insights of our own experiences as researchers conducting remote qualitative research and from the evidence from the literature on qualitative methods. The relevant literature was identified through searches using relevant keywords in the following databases: CINAHL, PubMed, SCOPUS, and Web of Science. Searches were limited to articles in English and Portuguese, published from 2010 to 2021, to ensure a current understanding of the phenomenon. RESULTS: Five tips are provided: 1) Pay attention to ethical issues; 2) Identify and select potential participants; 3) Choose the type of remote interview; 4) Be prepared to conduct the remote interview; and 5) Build rapport with the participant. CONCLUSION: Despite the challenges in conducting remote data collection, strengths are also acknowledged and our experience has shown that it is feasible to recruit and interview participants remotely. The discussions presented in this article will benefit, now and in the future, other research teams who may consider collecting data for their qualitative studies remotely.
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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 | 0.543 | 0.600 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.009 | 0.030 |
| Research integrity | 0.027 | 0.036 |
| 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".