Chronic Airway Disease Patients’ Challenges During a Virtual Study: Insights from Participants & Researchers
Bibliographic record
Abstract
Rationale: The COVID-19 pandemic has decreased the feasibility of in-person research. Despite this, widespread technological accessibility and ease-of-use make virtual research a viable alternative. Studies conducted virtually offer researchers and participants flexibility, convenience, and geographic accessibility. However, there are inherent challenges that may influence the research process, particularly for participants. This study examines patients' challenges with participating in a virtual research interview from the perspectives of researchers and patients. Methods: We conducted an exploratory community-based sub-study during a concurrent study to validate a function-based health literacy measurement tool (questionnaire) with chronic airway disease patients. Participants received and accessed study materials virtually and responded to the questionnaire over-the-phone; some participants were re-tested for questionnaire reproducibility. Initially, participants and researchers independently responded to 6 open-ended questions post-interview regarding challenges experienced during the study. Responses informed the development of a comprehensive, 18-question checklist addressing participant challenges. In subsequent interviews, research assistants administered the checklist, capturing quantitative data and verbatim quotes. Researchers' observation notes, recorded during each interview, and team teleconference notes were reviewed to provide researchers' perspectives on participant challenges. Thematic analysis of qualitative data was conducted using NVivo (QSR International, Version 12). Challenges were coded inductively and quantified on the basis of whether a researcher or patient had indicated a patient challenge, and cross-referenced with detailed observation notes. Quantitative analyses were conducted using R (R Core Team, 2019). Results: Initial interviews (n=185) and re-tests (n=110) were analysed. Quantified challenges were compared across self-reported gender, condition (asthma, chronic obstructive pulmonary disease (COPD) & asthma-COPD overlap (ACO)), education level, first language, age, ethnicity, disease duration, and previous disease-specific education. In 76% of interviews, participants experienced one or more challenges; 31 unique challenges were identified. As an example, Table Qualitative analysis revealed that participants primarily experienced challenges with technology, communication (verbal & online), and instruction clarity (e.g., when/how to use study materials). Conclusions: Participants experienced a variety of challenges throughout the virtual study. Considering potential friction points for participants and possible solutions prior to starting the study by involving researchers, key informants, and participants can enhance the study process. Developing simple, informative study materials and instructions may help to mitigate challenges inherent to virtual research and facilitate a seamless, participant-friendly study experience given the unfamiliarity of this research format.
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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 | 0.058 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".