Participant engagement in a national longitudinal study of COVID-19: Insights from the INSPIRE study
Bibliographic record
Abstract
OBJECTIVE: To examine participants' motivations and their experiences throughout a decentralized, longitudinal COVID-19 study in the U.S. METHODS: We recruited 355 participants from the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) between November 2022 - March 2023 to answer five qualitative survey questions anonymously. We used an inductive content analysis approach to analyze the data. RESULTS: We identified five key themes from the analysis, which reflected participants' a) motivations to join the study, b) study benefits, c) perceptions of survey questions, d) experiences with the research process, and e) preferences for disseminating research findings. Participants were motivated to learn with researchers about COVID-19. They expressed divided opinions about the relevance of INSPIRE research questions. They reported difficulties navigating the virtual research platform and the need for making survey participation less cognitively demanding. They sought more regular feedback on study findings. CONCLUSIONS: Our findings offered insights into incorporating decentralized participatory methods in longitudinal research, strengthening reciprocal research communications, making virtual research platforms user-friendly, and employing strategies to reduce participants' cognitive burden in research. POLICY IMPLICATIONS: Longitudinal studies should focus on optimizing these aspects of participant engagement to produce rigorous findings that inform policy and practice on lasting effects of COVID-19 including Long COVID.
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How this classification was reachedexpand
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".