Depressive symptoms in adults in rural and urban regions of Canada: a cross-sectional analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVES: Previous studies on depression in rural areas have yielded conflicting results. Features of rural areas may be conducive or detrimental to mental health. Our objective for this study was to determine if there are rural-urban disparities in depressive symptoms between those living in rural and urban areas of Canada. DESIGN: We conducted a cross-sectional analysis of a prospective cohort study, which is as representative as possible of the Canadian population-the Tracking Cohort of the Canadian Longitudinal Study on Aging. For this cohort, data were collected from 2010 to 2014. Data were analysed and results were obtained in 2020. PARTICIPANTS: 21 241 adults aged 45-85. MEASURES: Rurality was grouped as urban (n=11 772); peri-urban (n=2637); mixed (n=2125; postal codes with both rural and urban areas); and rural (n=4707). Depressive symptoms were measured using the 10-item Center for Epidemiological Studies-Depression. We considered age, sex, education, marital status and disease states as potential confounding factors. RESULTS: The adjusted beta coefficient was -0.24 (95% CI -0.42 to -0.07; p=0.01) for rural participants, -0.17 (95% CI -0.40 to 0.05; p=0.14) for peri-urban participants and -0.30 (95% CI -0.54 to -0.05; p=0.02) for participants in mixed regions, relative to urban regions. Risk factors associated with depressive symptoms were similar in rural and urban regions. CONCLUSIONS: The small differences in depressive symptoms among those living in rural and urban regions are unlikely to be relevant at a clinical or population level. The findings do suggest some possible approaches to reducing depressive symptoms in both rural and urban populations. Future research is needed in other settings and on change in depressive symptoms over time.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".