Measuring inequality in self‐reported health—discussion of a recently suggested approach using Finnish data
Bibliographic record
Abstract
Health surveys often include a general question on self-assessed health (SAH), usually measured on an ordinal scale with three to five response categories, from 'very poor' or 'poor' to 'very good' or 'excellent'. This paper assesses the scaling of responses on the SAH question. It compares alternative procedures designed to impose cardinality on the ordinal responses. These include OLS, ordered probit and interval regression approaches. The cardinal measures of health are used to compute and decompose concentration indices for income-related inequality in health. Results are provided using Finnish data on 15D and the SAH questions. Further evidence emerges for the internal validity of a method used in a pioneering study by van Doorslaer and Jones which was based on Canadian data on the McMaster Health Utility Index Mark III (HUI) and SAH. The study validates the conclusions drawn by van Doorslaer and Jones. It confirms that the interval regression approach is superior to OLS and ordered probit regression in assessing health inequality. However, regarding the choice of scaling instrument, it is concluded that the scaling of SAH categories and, consequently, the measured degree of inequality, are sensitive to characteristics of the chosen scaling instrument.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".