Contributors to mental health resilience in middle-aged and older adults: an analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVES: Identifying the correlates of mental health resilience (MHR)-defined as the discrepancy between one's reported current mental health and one's predicted mental health based on their physical performance-may lead to strategies to alleviate the burden of poor mental health in aging adults. Socioeconomic factors, such as income and education, may promote MHR via modifiable factors, such as physical activity and social networks. DESIGN: A cross-sectional study was conducted. Multivariable generalized additive models characterized the associations between socioeconomic and modifiable factors with MHR. SETTING: Data were taken from the population-based Canadian Longitudinal Study on Aging (CLSA), which collected data at various data collection sites across Canada. PARTICIPANTS: Approximately 31,000 women and men between the ages of 45 and 85 years from the comprehensive cohort of the CLSA. MEASUREMENTS: Depressive symptoms were assessed by the Center for Epidemiological Studies Depression Scale. Physical performance was measured objectively using a composite of grip strength, sit-to-stand, and balance performance. Socioeconomic and modifiable factors were measured by self-report questionnaires. RESULTS: Household income, and to a lesser extent, education were associated with greater MHR. Individuals reporting more physical activity and larger social networks had greater MHR. Physical activity accounted for 6% (95% CI: 4 to 11%) and social network accounted for 16% (95% CI: 11 to 23%) of the association between household income and MHR. CONCLUSIONS: The burden of poor mental health in aging adults may be alleviated through targeted interventions involving physical activity and social connectedness for individuals with lower socioeconomic resources.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".