Associations between Depression and Health Behaviour Change: Findings from 8 Cycles of the Canadian Community Health Survey
Bibliographic record
Abstract
Objectives:The primary objective of this study was to examine associations between depression and several measures of health behaviour change across 8 cycles of a population-based, cross-sectional survey of Canadians. The secondary objective of this study was to describe the prevalence of the types of health behaviour changes undergone/sought and types of barriers to change reported, comparing those with and without depression.Methods:The sample comprised 65,801 respondents to the Canadian Community Health Survey between 2007 and 2014. Past-year depression was assessed via structured interview (CIDI-SF). Measures of health behaviour change included recent changes made, desire to make changes, and barriers towards making changes. Analyses involved logistic regression, with estimates across cycles pooled using fixed-effects meta-analyses. Pooled prevalences of types of health behaviour changes undergone/sought and types of barriers to change experienced were reported, and associations with depression were examined.Results:Depression was associated with higher odds of reporting a recent health behaviour change (pooled odds ratio [OR] = 1.39; 95% confidence interval [CI], 1.30 to 1.48), desire to make health behaviour changes (pooled OR = 1.61; 95% CI, 1.49 to 1.74), and barriers towards change (pooled OR = 1.54; 95% CI, 1.44 to 1.65). The most common change undergone and sought was increased exercise; the most common barrier reported was a lack of willpower.Conclusions:Individuals dealing with depression are more likely to report recent health behaviour changes and the desire to make changes but are also more likely to report barriers towards change.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".