The moderating effect of sociodemographic factors on the predictive power of self-rated health for mortality in Canada
Bibliographic record
Abstract
Self-rated health is a reliable predictor for mortality, but its predictive power varies depending on social characteristics. This study tests the moderating effect of age, sex, education, and income on the power of self-rated health to predict mortality in Canada using data from the National Population Health Survey. Predictive power trajectories are modelled using time-series generalized estimating equation logistic regression. Findings show that self-rated health is a predictor for mortality up to 14 years prior to death in Canada, and is weakly moderated by income and education, and age/sex interactions. Self-rated health remains reliable across population sub-groups in Canada.La santé auto-évaluée est un prédicteur fiable de la mortalité, mais son pouvoir prédictif varie en fonction des caractéristiques sociales. Cette étude examine l'effet modérateur de l'âge, du sexe, de l'éducation, et du revenu sur le pouvoir de la santé auto-évaluée pour prédire la mortalité au Canada utilisant des données de l'Enquête nationale sur la santé de la population. Les trajectoires de puissance prédictive sont modélisées avec une régression logistique de l'équation d'estimation généralisée. Les résultats montrent que la santé auto-évaluée est un prédicteur de la mortalité jusqu'à 14 ans avant le décès au Canada, et est faiblement modérée par le revenu, l'éducation, et les interactions entre l'âge et le sexe. La santé auto-évaluée demeure valide parmi les sous-groupes de la population du Canada.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".