Het terugdringen van sociaal-economische verschillen in \ngezondheid tussen 2000 en 2020. Inhoud en organisatie van de SEGV monitor
Bibliographic record
Abstract
The Dutch government aims to reduce the socioeconomic health differences (SEGV) with a quarter of the current difference by the year 2020. To this purpose the health of different socioeconomic status groups needs to be monitored. The SEGV monitor will periodically report on the extent of socioeconomic differences at the national level in the Netherlands. Health determinants, such as health-related behaviour, environmental factors and healthcare use will also be monitored. Because many policies and interventions are developed and carried out at the local level, it would be desirable to be able to draw conclusions on socioeconomic health differences at this level or to have information on the development in health in certain neighbourhoods. The SEGV monitor will make use of existing data sources with nation-wide coverage to generate a representative and valid picture of the development of socioeconomic health differences in the Netherlands. The SEGV monitor is to report on socioeconomic differences in health and its determinants every four years. Each four-yearly report will be accompanied by an elaborate supplement on one specific current subject. Results of the SEGV monitor will be accessible through the Internet via a thematic link on the Dutch-language website, 'Nationaal Kompas Volksgezondheid'.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.012 |
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".