Declines and Regains in Income Status and Health Status Among Mid- and Later-life Canadians
Bibliographic record
Abstract
As Canada's population ages, understanding the associated economic and social issues that may emerge becomes critical. This study's purpose was to investigate sociodemographic and health behaviour factors that influence income and/or health changes among Canadians in mid- and later-life. To examine these factors, logistic regression analyses were undertaken using a representative sample (n = 2,368) of Canadians, ages 40 to 59 from seven cycles (1994-1995 to 2006-2007) of longitudinal data from the National Population Health Survey. This study examined whether, for this age group, income decline was a stronger determinant of health decline (social causation) than vice versa (reverse causation). Of 382 respondents who experienced both an income and health decline, 230 experienced an income before a health decline. Several logistic regression findings supported social causation and only one supported reverse causation. Also explored by this study was the comparative influence of sociodemographic versus health behaviour factors on changes in income and health. Compared to sociodemographic factors, health behaviour factors had less influence on changes in income and health. The physical inactivity and obesity variables were infrequently statistically significant predictors of income and health changes. The drinking habits variable was frequently a statistically significant predictor of changes in income and health. A history of smoking was very frequently a statistically significant predictor--of health declines only, both income and health declines, and income declines before health declines.The factors associated with * two-fold declines (income and health) were higher income, good or very good (vs. excellent) health, being older, not having graduated from high school, and a history of smoking; * income regains were lower income, being male, younger, married, and a high school graduate; and * health regains were higher income, being neither a high school graduate nor an immigrant, never having smoked, and being a moderate or nondrinker. A better understanding of the patterns and predictors of income and health declines and regains among mid- and later-life Canadians may serve to identify opportunities to improve the future welfare of the elderly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".