Research recruitment and consent methods in a pandemic: a qualitative study of COVID-19 patients’ perspectives
Bibliographic record
Abstract
BACKGROUND: Virtual data collection methods and consent procedures adopted in response to the COVID-19 pandemic enabled continued research activities, but also introduced concerns about equity, inclusivity, representation, and privacy. Recent studies have explored these issues from institutional and researcher perspectives, but there is a need to explore patient perspectives and preferences. This study aims to explore COVID-19 patients' perspectives about research recruitment and consent for research studies about COVID-19. METHODS: We conducted an exploratory qualitative focus group and interview study among British Columbian adults who self-identified as having had COVID-19. We recruited participants through personal contacts, social media, and REACH BC, an online platform that connects researchers and patients in British Columbia. We analyzed transcripts inductively and developed thematic summaries of each coding element. RESULTS: Of the 22 individuals recruited, 16 attended a focus group or interview. We found that autonomy and the feasibility of participation, attitudes toward research about COVID-19, and privacy concerns are key factors that influence participants' willingness to participate in research. We also found that participants preferred remote and virtual approaches for contact, consent, and delivery of research on COVID-19. CONCLUSIONS: Individuals who had COVID-19 are motivated to participate in research studies and value autonomy in their decision to participate, but researchers must be sensitive and considerate toward patient preferences and concerns, particularly as researchers adopt virtual recruitment and data collection methods. Such awareness may increase research participation and engagement.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | medium |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
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 | 0.081 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".