Evaluation of Health Interview Surveys and Health Examination Surveys in the European Union
Bibliographic record
Abstract
BACKGROUND: The project 'Health surveys in the EU: HIS and HIS/HES evaluations and models' aims to assess the coverage of specific health and health-related areas in national and international surveys by reviewing and evaluating surveys, their methods and comparability, and by recommending appropriate survey designs and methods. METHODS: As basis for the evaluation, the project developed a health survey database. At present, Health Interview Surveys (HIS) and Health Examination Surveys (HES) from 18 Western European countries as well as from Canada, Australia and the USA are included. RESULTS: National HISs have been carried out regularly in almost all Western European countries. The HIS may consist of short health sections or modules within multi-purpose surveys or lengthy health interviews with several questionnaires. National HESs with a comprehensive focus have been conducted at regular or irregular intervals in five countries. The HES may comprise an interview and a few measurements or a comprehensive health examination. Sampling frames, fieldwork, quality control procedures and response rates vary greatly. Differences between measurement instruments used, in the wording of questions and in examination protocols reduce the comparability of many findings. CONCLUSION: The Internet based HIS/HES database allows for a quick reference and comparison of methods and instruments used in national health surveys. It illustrates the need for improving comparability. Collaboration and co-ordination is needed to promote comprehensive health monitoring supporting the development of national and European-level health policy.
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 | 0.645 | 0.639 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier 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".