EuroQol data for assessment of population health needs and instrument evaluation (EQ-DAPHNIE): a study for enhancing population health assessment
Bibliographic record
Abstract
BACKGROUND: Methods for collecting self-reported health status measures in population health surveys vary significantly across countries, presenting challenges to comparability. The EuroQol Data for Assessment of Population Health Needs and Instrument Evaluation (EQ-DAPHNIE) project aims to address this issue by developing infrastructure to generate representative datasets across multiple countries. This initiative aims to standardize data collection methodologies and to evaluate the performance of various health status measures, providing a foundation for reliable population health assessments. This paper describes the rationale, design and data collection methods for the EQ-DAPHNIE project. METHODS/DESIGN: EQ-DAPHNIE employs a cross-sectional online survey design targeting the general adult population across various countries. Participants were recruited through an online panel provider. Each country had a target sample of 4500 responses, with quota sampling to ensure representativeness based on age, sex, income, region, and language. The survey collected comprehensive data on social determinants of health at both individual and neighbourhood levels. Participation was voluntary, and measures were taken to maintain data anonymity and ensure data quality through pre-testing and various quality assurance approaches. DISCUSSION: The EQ-DAPHNIE project represents a significant advancement in generating large, representative, and comparable population health datasets across multiple countries. By employing precise sampling strategies, robust recruitment and data collection methods, and rigorous quality control measures, the project aims to provide a valuable resource for assessing and understanding population health and evaluating various health-related quality of life (HRQoL) and wellbeing instruments.
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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 | 0.124 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".