Survey Research on Health Inequalities: Exploring the Availability of Indicators of Multiple Forms of Capital in Canadian Datasets
Bibliographic record
Abstract
Objective: Much of the extensive quantitative research linking socio-economic position (SEP) and health utilizes three common indicators: income, occupation and education. Existing survey data may enable researchers to include indicators of additional forms of capital in their analyses, permitting more nuanced consideration of the relationship between SEP and health. Our objective was to identify the breadth of survey questions related to economic, cultural, and social capital available through Statistics Canada surveys, and the extent to which those surveys also include health measures. Methods: We compiled a list of all population-based Statistics Canada surveys, and developed a broad list of potential indicators of forms of capital. We systematically searched the surveys for those indicators and health measures, analyzing their co-occurrence. Results: Traditional SEP indicators were present in 73% of surveys containing health measures, while additional indicators of social and cultural capital were available in 57%. Conclusion: Existing national survey data represent an under-exploited opportunity for research examining the relationship between various forms of capital and health in Canada. Future empirical explorations of these data could enrich our theoretical understanding of health inequities.
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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 | 0.029 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.038 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".