Relationships between psychological distress and health behaviors among Canadian adults: Differences based on gender, income, education, immigrant status, and ethnicity
Bibliographic record
Abstract
OBJECTIVE: Psychosocial health predicts physical health outcomes in both clinical samples and the general population. One mechanism is through relationships with health behaviors. Results might differ based on sociodemographic characteristics such as education, income, ethnicity, and immigrant status. Our objective was to analyze sociodemographic differences in relationships between psychosocial health measures and health behaviors in the general population of Canadian adults. METHODS: We analyzed relationships between non-specific psychological distress, assessed using the Kessler-10 scale, and five key health behaviors: fruit and vegetable intake, screen sedentary behavior, physical activity, alcohol consumption, and cigarette use. Data were collected by Statistics Canada for the Canadian Community Health Survey in 2011-2014. Our sample included 54,789 participants representative of 14,555,346 Canadian adults. We used univariate general linear models on the weighted sample to analyze relationships between distress (predictor) and each health behavior, controlling for age. We entered sex and one of four sociodemographic variable of interest (education, income, ethnicity, immigrant status) into each model to analyze gender and sociodemographic differences in relationships. RESULTS: up to 0.013). Differences by gender and sociodemographic characteristics were evident for all health behaviors. CONCLUSIONS: Psychosocial health might contribute to persistent socioeconomic disparities in health in part through relationships with health behaviors, although relationships in the general population are modest. Health behavior interventions incorporating psychosocial health might need to be tailored based on socioeconomic characteristics, and future research on intersections between multiple sociodemographic risk factors remains necessary.
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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) | 0.004 | 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".