Examining early adversities, demographic, health and psychosocial factors associated with lifetime depression among older Canadians: Findings from a nationally representative study
Bibliographic record
Abstract
Depression among older adults is associated with greater negative physical health, social, and economic outcomes than in younger populations. The objective of this study was to examine factors associated with lifetime depression in a nationally representative sample of Canadian older adults, highlighting characteristics linked to both vulnerability and resilience. Secondary analysis of the 2022 Mental Health Access to Care Survey (MHACS) was conducted to estimate the prevalence and factors associated with lifetime depression among adults aged 55 and older ( n = 3,535). The MHACS measured depression using the World Health Organization’s Composite International Diagnostic Interview (WHO-CIDI). Multivariable logistic regression of lifetime depression was conducted analyzing demographic and socioeconomic variables, adverse childhood experiences, physical health measures, health behaviors, and protective psychosocial factors. One in eleven older adults (9.2%) had experienced depression at some point in their lives. Middle-aged adults (55–64 years) compared to older respondents, and females compared to males had twice the odds of lifetime depression. Other factors associated with depression included childhood physical or sexual abuse, higher educational attainment, history of substance use disorders, multiple chronic physical health conditions, lower sense of life meaning, and higher spirituality. Lifetime depression in older adults is associated with a complex interplay of risk and protective factors. Identifying these factors can support early recognition and targeted intervention, potentially improving outcomes and quality of life in this population.
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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 | 0.004 |
| 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) | 0.001 | 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".