Health impacts of social determinants and lifestyle behaviours: some evidence from Canadian provinces
Bibliographic record
Abstract
This study seeks to identify the impact of social determinants and lifestyle factors on life expectancy and self-perceived health as two measures of objective and subjective health, respectively, using data from Canadian provinces during 2007-21. Through a simple conceptual model, it lays out the direct and indirect pathways through which social and lifestyle determinants affect health. The conceptual model guides the formulation of empirical models, which are used to estimate the effects of social and lifestyle factors on health. The study uses 'panel-corrected standard errors' estimation method to obtain reliable results. The findings confirm that social determinants contribute directly and indirectly (through lifestyle) to life expectancy. For self-perceived health, however, the contributions of both social and lifestyle determinants are only direct. The latter result may be explained by the immediacy of lifestyle and its impact on health in individuals' minds, a notion that is constantly reinforced by the mainstream discourse on health promotion. Our study indicates that lifestyle factors should be addressed within the context of broader social determinants. In other words, an individual agency should be understood within the larger enveloping social structure. The study recognizes redistributive measures aimed at reducing social gradients in health as effective health promotion policies.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".