Relationships between psychological distress and health behaviors among Canadian adults: Differences based on gender, income, education, immigrant status, and ethnicity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it