Mood disorders among older Canadians
Bibliographic record
Abstract
Background: An increasing number of Canadians are living with mental health problems, including mood disorders. However, few studies have examined the prevalence of, and factors associated with, mood disorders among older Canadians (65 years or older). Data and methods: A pooled sample of 172,524 community-dwelling older Canadians from nine cycles of the annual Canadian Community Health Survey - 2015 to 2023 - was used to examine mood disorders and associated correlates. Multivariable logistic regression, stratified by sex, was implemented to identify factors associated with mood disorders. Results: From 2015 to 2023, on average, 7.0% of older Canadians reported a diagnosis of a mood disorder, with females (8.3%) more likely than males (5.5%) to do so. In a multivariable analysis that adjusted for demographic, socioeconomic, geographic, and health-related factors, Indigenous people (males and females) had higher odds of having a mood disorder than non-Indigenous, non-racialized populations. South Asian and Chinese males, as well as females belonging to Black and Other racialized groups, had significantly lower odds compared with their non-Indigenous, non-racialized counterparts. Living alone, being a male immigrant, and having lower household income were associated with a higher likelihood of experiencing mood disorders among older Canadians. Interpretation: The results of this study highlight the importance of considering racialized population groups, as well as socioeconomic, geographic, and health-related factors - separately for males and females - when examining mood disorders among older Canadians to inform screening and intervention programs.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".