Focus and methodological adaptations of qualitative research during the COVID-19 pandemic: a scoping review and textual narrative synthesis
Bibliographic record
Abstract
Research is a vital driver of the response to health emergencies. This scoping review aimed to characterize the application of qualitative research during the COVID-19 pandemic, with two primary objectives: identifying the qualitative research methods and adaptations applied, and summarizing the research questions which the studies sought to answer. CINAHL and PsycINFO were systematically searched for qualitative studies relating to COVID-19 and published between January 2020 and November 2021. Articles were screened and included in the review using pre-defined eligibility criteria. A total of 535 articles met the inclusion criteria, mostly from North America and Europe. An observed methodological adaptation was a surge in virtually conducted research – most studies collected data through interviews, 52% of which were conducted virtually using telephone or teleconferencing technology. Similarly, 27% of the focus group discussions and 20% of the ethnographies were conducted virtually. A textual narrative synthesis of all reviewed studies identified four major groups: health-related studies, education-related studies, studies about vaccine acceptance, and studies in specific population groups, such as the elderly, ethnic minorities, and working-class women in patriarchal contexts. There was a seeming neglect of the experience of youths, and insufficient attention has been paid to the dynamics of medical distrust with regard to vaccine hesitancy. Qualitative research has been applied to extensively explore people’s perceptions and experiences of the pandemic. The progressive improvement of virtual research methods will be beneficial for future pandemic preparedness. More representation of research from under-resourced regions of the world is also needed.
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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 | 0.342 | 0.446 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.042 | 0.038 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".