Exploring Diversity in Socioeconomic Inequalities in Health Among Rural Dwelling Canadians
Bibliographic record
Abstract
PURPOSE: To describe the patterning of socioeconomic inequalities in health among rural dwelling women and men in a Canadian province, exploring diversity in associations by measure of socioeconomic position, health outcome, and demographic characteristics. METHODS: Baseline data from the Saskatchewan Rural Health Study was used, an ongoing prospective cohort study examining the health of rural people in Saskatchewan, Canada. Of the 11,004 eligible addresses, responses to mailed questionnaires were obtained from 4,624 (42%) households, representing 8,261 women and men. Multiple logistic regression was the primary method of analysis; generalized estimating equations were utilized to account for household clustering. Associations between 5 health outcomes (self-rated health, chronic obstructive lung disease, diabetes, heart attack, high blood pressure) and 4 indicators of socioeconomic position (income, education, financial strain, occupational skill level) were assessed, with age and gender as potential effect modifiers. FINDINGS: With the exception of occupational skill level, socioeconomic position (SEP) indicators were strongly and inversely related to most health outcomes, often in a graded manner. Associations between SEP and several health outcomes were weaker for older than younger participants (heart attack, high blood pressure, lung disease) and stronger among women compared to men (high blood pressure, lung disease). CONCLUSIONS: The patterning of SEP-health associations observed in this rural Canadian sample suggests the need for health promotion strategies and policy initiatives to be broadly targeted at individuals and families occupying a wide range of socioeconomic circumstances.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.001 | 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".