Platform matters -- Differences in COVID data collected from Android and iOS app users
Bibliographic record
Abstract
While it has been five years since the start of the COVID-19 pandemic, and many have turned their attention away from this global health crisis, Winter et al.’s recent study “A Comparison of Self-Reported COVID-19 Symptoms Between Android and iOS CoronaCheck App Users” 1 demonstrates that data collected during COVID-19 remain pertinent in guiding broader mobile health research. In their recent study, Winter et al. 1 analyze data collected by CoronaCheck, an application for users to describe symptoms and exposure status, and receive COVID-19 risk-stratification guidance. Analysis of CoronaCheck data provides insight into patterns of COVID-19 symptoms and infection rates. However, the significance of Winter et al.’s findings is not limited in scope to the pandemic. Rather, by identifying differences in demographics and symptoms reported by Android and iOS users, Winter et al. 1 reveal that data collected from one platform may not be generalizable to users of other platforms.
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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 | 0.017 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".