Cross-sectional and longitudinal relationships between COVID-19 stressors and depressive symptoms across sex and age groups: findings from the Canadian longitudinal study on aging
Bibliographic record
Abstract
AIMS: This study employs a longitudinal network approach to investigate the dynamic relationships between COVID-19-related stressors and depressive symptoms among Canadian adults and to explore any sex and age differences in these associations. METHODS: The study utilised data from the Canadian Longitudinal Study on Ageing (CLSA), a large, national, long-term study of Canadian adults aged 45 years and older. Depressive symptoms were measured using the Centre for Epidemiologic Studies Depression Scale (CES-D), and COVID-19-related stressors were evaluated using a standardised stress inventory adapted for the pandemic context. The cross-lagged panel network analysis (CLPN) was employed to examine the temporal relationships and dynamic interactions between depressive symptoms and COVID-19-related stressors. RESULTS: Significant variations in network structures and strengths were identified across demographic groups. Individuals aged between 45 and 65 years and females exhibited stronger connections between COVID-19-related stressors and depressive symptoms. Central symptoms such as "feeling unhappy" were consistent across groups, while "feeling depressed" was more central among males and "increased verbal or physical conflict" among females. Additionally, health-related stressors and family separation emerged as critical bridge symptoms for males and individuals under 65 years, respectively. CONCLUSIONS: Both cross-sectional and longitudinal relationships, and directionality between COVID-19-related stressors and depressive symptoms across sex and age groups were identified. The findings of the study highlight that dedicated mental health intervention and prevention efforts are warranted to ameliorate the negative impact of stressors on depressive symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".