COMPARATIVE ANALISYS OF ANALISYS OF HEALTH SURVEYS
Bibliographic record
Abstract
One of aims of health surveys is comparison helth status of residents on different territories. Comparative analysis оf health surveys in five countries: (England, USA, Ireland, Canada and Hungary) and establish opportunities for comparison health status of residents in different country. Will be doing comparative analysis of results from health surveys in five quoted country. First of all, will be doing comparison of methods and derived results. Specially, will be notice on determinants of health which are applied in surveys. Methodology which was used is, mainly, similar in all country. Its health state like a best describes residents of USA. There are most smokers among Hungarians (30,5%) and least of all among Americans (17%). There are most obeses among residents of England (23,1%) and least of all among Canadians (15%). Most Americans and Hungarians (85%) visited general practitioners in recent year. Questions about limited mobility, depression, mental health, cardiovascular diseases, diabetes, blood pressure, fisical activity, mammography, prescribe medicine, estimation quality of health care service and satisfaction with health care service there isn't in most of surveys. We can conclude that the methodology, which was used in surveys, is mainly similar. There are, obviously, variances in wording questions, respecting in determinants of health wich are exploratives in questionnaires. In order to escape these variances, one of the solution would have been using standardized questionnaires, which will be using in future surveys in all country.
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 | 0.131 | 0.375 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".