Measuring Socioeconomic Health Inequalities in Presence of Multiple Categorical Information
Bibliographic record
Abstract
While many of the measurement approaches in health inequality measurement assume the existence of a ratio-scale variable, most of the health information available in population surveys is given in the form of categorical variables. Therefore, the well-known inequality indices may not always be readily applicable to measure health inequality as it may result in the arbitrariness of the health concentration index's value. In this paper, we address this problem by changing the dimension in which the categorical information is used. We therefore exploit the breadth of this information, define a new ratio-scaled health status variable and develop positional stochastic dominance conditions that can be implemented in a context of categorical variables. We also propose a parametric class of population health and socioeconomic health inequality indices. Finally we provide a twofold empirical illustration using the Joint Canada/United States Surveys of Health 2004 and the National Health Interview Survey 2010. / Bien que plusieurs approches à la mesure des inégalités de santé font comme hypothèse qu’il existe une variable d’échelle de ratio, l’information que l’on retrouve dans les enquêtes sur la santé de la population prend souvent la forme de variables catégoriques. Dans ce contexte, les indices d’inégalité connus peuvent ne pas être directement applicables à la mesure des inégalités de santé puisqu’ils produiront des valeurs arbitraires. Dans cet article, nous apportons une solution à ce problème en changeant la dimension dans laquelle l’information catégorique est utilisée. Nous exploitons l’étendue de cette information afin de définir une nouvelle variable d’échelle de ratio et nous développons des tests de dominance stochastique qui peuvent être ainsi implémentés dans le contexte de variables catégoriques. Nous proposons aussi une classe paramétrique d’indices de santé de la population et d’indices d’inégalité socioéconomique de santé. Finalement, nous proposons deux illustrations basées sur l’Enquête conjointe Canada/États-Unis sur la santé et le National Health Interview Survey 2010.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".