Ethical Considerations for Qualitative Research Methods During the COVID-19 Pandemic and Other Emergency Situations: Navigating the Virtual Field
Bibliographic record
Abstract
Qualitative research is integral to the pandemic response. Qualitative methods are ideally suited to generating evidence to inform tailored, culturally appropriate approaches to COVID-19, and to meaningfully engaging diverse individuals and communities in response to the pandemic. In this paper, we discuss core ethical and methodological considerations in the design and implementation of qualitative research in the COVID-19 era, and in pivoting to virtual methods—online interviews and focus groups; internet-based archival research and netnography, including social media; participatory video methods, including photo elicitation and digital storytelling; collaborative autoethnography; and community-based participatory research. We identify, describe, and critically evaluate measures to address core ethical challenges around informed consent, privacy and confidentiality, compensation, online access to research participation, and access to resources during a pandemic. Online methods need not be considered unilaterally riskier than in-person data collection; however, they are clearly not the same as in-person engagement and require thoughtful, reflexive, and deliberative approaches in order to identify and mitigate potential and dynamically evolving risks. Ensuring the ethical conduct of research with marginalized and vulnerable populations is foundational to building evidence and developing culturally competent and structurally informed approaches to promote equity, health, and well-being during and after the pandemic. Our analysis offers methodological, ethical, and practical guidance in the COVID-19 pandemic and considerations for research conducted amid future pandemics and emergency 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.
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 | 0.578 | 0.539 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.061 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".