Income-related health inequality in Canada
Bibliographic record
Abstract
This study uses data from the 1994 National Population Health Survey and applies the methods developed by Wagsta and van Doorslaer (1994, measuring inequalities in health in the presence of multiple-category morbidity indicators. Health Economics 3, 281–291) to measure the degree of income-related inequality in self-reported health in Canada by means of concentration indices. It finds that significant inequalities in self-reported ill-health exist and favour the higher income groups — the higher the level of income, the better the level of self-assessed health. The analysis also indicates that lower income individuals are somewhat more likely to report their self-assessed health as poor or less-than-good than higher income groups, at the same level of a more ‘objective ’ health indictor such as the McMaster Health Utility Index. The degree of inequality in ‘subjective ’ health is slightly higher than in ‘objective’ health, but not significantly dierent. The degree of inequality in self-assessed health in Canada was found to be significantly higher than that reported by van Doorslaer et al. (1997, income related inequalities in health: some international comparisons, Journal of Health Economics 16, 93–112) for seven European countries, but not significantly dierent from the health inequality measured for the UK or the US. It also appears as if Canada’s health inequality is higher than what would be expected on the basis of its income inequality. # 2000 Elsevier Science Ltd. All rights reserved.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".