Determinants of self-perceived health for Canadians aged 40 and older and policy implications
Bibliographic record
Abstract
BACKGROUND: Perceived health status indicates people's overall perception of their health, including both physical and psychological dimensions. The aim of this study was to examine the determinants of self-perceived health for Canadians aged 40 and older using data from the Canadian Community Health Survey (2010). METHODS: Multiple logistic regression models were employed to identify factors associated with self-perceived health in two age groups: Adults aged 65+ and Adults aged 40-64. RESULTS: We found that higher income was significantly associated with better health status while chronic conditions and stress were associated with worse health status. In the 40-64 and 65+ age groups, individuals in the highest income bracket were 4.65 and 1.94 times, respectively, more likely to report better health than individuals in the lowest income bracket. The difference in the level of income associated health inequities between the two age groups point to the need for understanding the reasons behind lower inequities among seniors and how much the social protections provided by the Canadian government to seniors contribute to lowering inequities. CONCLUSIONS: Though Canada has a national public health insurance system providing coverage to all Canadians, health inequities associated with income persist providing further evidence of the importance of the social determinants of health. Examining the extent of these inequities and what factors influence them helps direct policy attention. In addition to documenting inequities, this paper discusses policy options for reducing the identified 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".