Five tips for conducting remote qualitative data collection in COVID times: theoretical and pragmatic considerations
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
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.
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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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