Bibliographic record
Abstract
Purpose: Numerous studies have estimated health disparities along socioeconomic dimensions using individual data from sample surveys. Disparities between communities or regions cannot be estimated without a consistent set of individual data across communities. This study uses data at the health region level to estimate the socioeconomic health disparitiesbetweenhealth regions in Canada. Methods: Tow measures of income and a measure of education are used for regional socioeconomic ranking along with several health outcomes such as life expectancies, mortality rates, perceived health and obesity. Weighted regressionanalysis is used to estimate the relative inequality index (RII) between Canadian health regions. Results: The findings of the study indicate the existence of health disparities between Canadian health regions along the three socioeconomic markers of average income, median household income and education in favor of regions with higher socioeconomic ranking on those markers. Disparities are more pronounced along the education and average income dimensions, however. Greater inequalities are observed for premature mortality, avoidable mortality and obesity, which are higher for women than men. Conclusion: There are health disparities between Canadian health regions along education and income dimensions. Such disparities signify the role of socioeconomic factors as important instruments in reducing health disparities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".