Social relationships and depression among people 65 years and over living in rural and urban areas of Quebec
Bibliographic record
Abstract
OBJECTIVE: To compare the prevalence of depression within the elderly Quebec population residing in rural areas, urban areas and metropolitan Montreal, and to assess differences in the associations between social relationships and depression across these urban and rural settings. METHODS: Data originate from the first wave of the ESA (Etude de Santé des Ainés) longitudinal study on mental health of community dwelling older persons aged over 65 (n = 2670). Depression, including major and minor depression, measured using a computer questionnaire; the ESA-Q developed by the research team and based on the DSM-IV criteria. Assessments of associations between depression and geographic area, informal social networks and community participation were estimated adjusting for demographic, socioeconomic and health characteristics. RESULTS: The prevalence of depression was higher in rural (17%) and urban areas (15.1%) than in metropolitan Montreal (10.3%). The odds ratio of rural (OR = 2.01 95% CI 1.59-2.68) and urban (OR = 1.75; 95% CI 1.25-2.45) areas compared to the metropolitan area increased slightly after adjustment by all social and health covariates. Our study indicated that social support and the lack of conflict in intimate relationships were associated with lower prevalence of depression in all areas. CONCLUSION: Geographic differences in depression exist within the elderly population in Quebec that may generate significant impact on their health and functional abilities. Further research should be conducted to explain these differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".