Integrating Socio-Economic Determinants of Canadian Women's Health
Bibliographic record
Abstract
HEALTH ISSUE: The association between a number of socio-economic determinants and health has been amply demonstrated in Canada and elsewhere. Over the past decades, women's increased labour force participation and changing family structure, among other changes in the socio-economic environment, have altered social roles considerably and lead one to expect that the pattern of disparities in health among women and men will also have changed. Using data from the CCHS (2000), this chapter investigates the association between selected socio-economic determinants of health and two specific self-reported outcomes among women and men: (a) self-perceived health and (b) self-reports of chronic conditions. KEY FINDINGS: The descriptive picture demonstrated by this CCHS dataset is that 10% of men aged 65 and over report low income, versus 23% of women within the same age bracket. The results of the logistic regression models calculated for women and men on two outcome variables suggest that the selected socio-economic determinants used in this analysis are important for women and for men in a differential manner. These results while supporting other results illustrate the need to refine social and economic characteristics used in surveys such as the CCHS so that they would become more accurate predictors of health status given that there are personal, cultural and environmental dimensions to take into account. RECOMMENDATIONS: Because it was shown that socio economic determinants of health are context sensitive and evolve over time, studies should be designed to examine the complex temporal interactions between a variety of social and biological determinants of health from a life course perspective. Examples are provided in the chapter.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".