The COVID-19 Citizen Science Study: Protocol for a Longitudinal Digital Health Cohort Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has catalyzed a global public response and innovation in clinical study methods. OBJECTIVE: The COVID-19 Citizen Science study was designed to generate knowledge about participant-reported COVID-19 symptoms, behaviors, and disease occurrence. METHODS: COVID-19 Citizen Science is a longitudinal cohort study launched on March 26, 2020, on the Eureka Research Platform. This study illustrates important advances in digital clinical studies, including entirely digital study participation, targeted recruitment strategies, electronic consent, recurrent and time-updated assessments, integration with smartphone-based measurements, analytics for recruitment and engagement, connection with partner studies, novel engagement strategies such as participant-proposed questions, and feedback in the form of real-time results to participants. RESULTS: As of February 2021, the study has enrolled over 50,000 participants. Study enrollment and participation are ongoing. Over the lifetime of the study, an average of 59% of participants have completed at least one survey in the past 4 weeks. CONCLUSIONS: Insights about COVID-19 symptoms, behaviors, and disease occurrence can be drawn through digital clinical studies. Continued innovation in digital clinical study methods represents the future of clinical research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28169.
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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 | 0.033 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.022 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads 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".