Additional file 1 of Projecting years in good health between age 50–69 by education in the Netherlands until 2030 using several health indicators - an application in the context of a changing pension age
Bibliographic record
Abstract
Additional file 1: Appendix Table 1. Summary of construction of the education variable using the Dutch Health Interview survey (1989–2018). Appendix Table 2. Sample size for individuals aged 50–69 from the Dutch Health Survey (1989–2018), by gender and year. Appendix 3. Extrapolation of mortality rates for age groups 50–54, 55–59, 60–64, 65–69 by gender and education. Appendix 4. Test for non-linearity and inclusion of three way-interaction term. Appendix Table 5. Age standardized prevalence and predicted prevalence of several health indicators, by gender, education level and year. Appendix Table 6. Years in good health for several health indicators for individuals aged 50–69, by gender, education and year. Appendix Table 7. Surplus or deficit of years in good health relative to the statutory retirement age in the Netherlands and educational inequalities, by gender, education and year. Appendix 8. Partial Life expectancy, healthy life years and `deficit’ or `surplus’ for the medium educated between ages 50–69 by gender. Appendix Table 9. Robustness - `Deficit’ and `Surplus’ of years in good health relative to the retirement age for different health indicators for individuals between 50 and 69 by year, gender, education and related educational inequalities – Alternative scenarios.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.697 | 0.077 |
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".