Observational Cross-Sectional Study to Estimate Population Norms in Eight Countries: the POPUP Study Protocol
Bibliographic record
Abstract
Abstract This study protocol outlines the Population Norms Study (POPUP), a multinational digital survey aimed at establishing general population norms for medical resource use, comorbidities, sick leave, caregiver support, quality of life, and functioning across eight countries: United States (US), Canada, United Kingdom (UK), the Netherlands, Belgium, Spain, Italy, and Germany. Data will be collected through an online self-administered survey in two waves: the first in Q1 2021 and the second in Q1 2023, when first-wave responders will be recontacted. A total of 15,500 responses will be gathered across both waves: 9,000 in the first wave and 4,500 re-contacts in the second, with an additional 2,000 new contacts if needed. Representative panels will be recruited by a research company from each country based on age, gender, education, and region. Participants will complete the survey after providing informed consent. No personal identifiers will be recorded. The observational study involves no medical interventions, drugs, or devices, and will be submitted for Ethical Committee approval in each country. Descriptive statistics will be used for data analysis. The results are expected to provide a baseline for comparing health outcomes in specific patient populations and quantifying disease burden.
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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 | 0.040 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".