and the HIS/HES Core Group (see Annex 1)
Bibliographic record
Abstract
The project on Health Surveys in the EU supports health monitoring by developing a computerised health survey database, by reviewing and evaluating surveys, their methods and comparability, by recommending designs and methods, and by disseminating this information. It also assesses the coverage of specific health and health related areas in national and international surveys. At present, Health Interview Surveys (HIS) and Health Examination Surveys (HES) are included from 18 Western European countries as well as Canada, Australia and USA. National HISs are carried out regularly in almost all Western European countries. National HESs with a comprehensive focus are conducted at regular or irregular intervals in five countries. The HIS may consist of short health sections or modules within multi-purpose surveys or lengthy health interviews with several questionnaires. The HES (or HIS/HES) may comprise an interview with a few measurements or a comprehensive health examination. There are important differences in sampling frames, in fieldwork, and in quality control procedures. The response rates vary greatly. Differences in instruments used, in the wordings and in survey protocols reduce the comparability of many topics. The interactive Internet based HIS/HES database allows for a quick reference and comparison of methods and instruments used in national health surveys. It also illustrates the great variety in instruments and protocols, and the need for harmonisation. Collaboration and co-ordination is needed to promote comprehensive health monitoring at the European level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.122 | 0.061 |
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".