Developing a Socioeconomic Status Index for Chronic Disease Prevention Research in Canada
Bibliographic record
Abstract
Capturing socioeconomic inequalities in relation to chronic disease is challenging since socioeconomic status (SES) encompasses many aspects. We constructed a comprehensive individual-level SES index based on a broad set of social and demographic indicators (gender, education, income adequacy, occupational prestige, employment status) and examined its relationship with smoking, a leading chronic disease risk factor. Analyses were based on baseline data from 17,371 participants of Alberta’s Tomorrow Project (ATP), a prospective cohort of adults aged 35−69 years with no prior personal history of cancer. To construct the SES index, we used principal component analysis (PCA) and to illustrate its utility, we examined the association with smoking intensity and smoking history using multiple regression models, adjusted for age and gender. Two components were retained from PCA, which explained 61% of the variation. The SES index was best aligned with educational attainment and occupational prestige, and to a lesser extent, with income adequacy. In the multiple regression analysis, the SES index was negatively associated with smoking intensity (p < 0.001). Study findings highlight the potential of using individual-level SES indices constructed from a broad set of social and demographic indicators in epidemiological research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".