Measurement of Health, the Sensitivity of the Concentration Index, and Reporting Heterogeneity
Bibliographic record
Abstract
Using representative survey data from the German Socio-Economic Panel Study (SOEP) for 2006, we show that the magnitude of such health inequality measures as the concentration index (CI) depends crucially on the underlying health measure. The highest degree of inequality is found when dichotomized subjective health measures like health satisfaction or self-assessed health (SAH) are employed. Measures of medical care usage like doctor visits result in substantially lower concentration indices. Moreover, with the use of SF12, a generic health measure, the inequality indicator is reduced by a factor of ten. Scaling SAH by means of the SF12 leads to similar results to those with the pure SF12 measure. Employing generic health measures used with other populations like the Canadian HUI-III or the Finish 15D to cardinalize SAH has a significant impact on the degree of inequality measured. Finally, by contrasting the physical health component of the SF12 to the unambiguously objective grip strength measure, we provide evidence of the presence of income-related reporting heterogeneity in generic health measures.
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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 | 0.039 | 0.215 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".