Multimorbidity, COVID-19 and Mental Health: Canadian Longitudinal Study on Aging (CLSA) Longitudinal Analyses
Bibliographic record
Abstract
OBJECTIVES: This paper examines the longitudinal effects of the COVID-19 pandemic on older adults (65+) with multimorbidity on levels of depression, anxiety, and perceived global impact on their lives. METHODS: Baseline (2011-2015) and Follow-up 1 (2015-2018) data from the Canadian Longitudinal Study on Aging (CLSA), and the Baseline and Exit waves of the CLSA COVID-19 study (April-December, 2020) (n = 18,099). Multimorbidity was measured using: a) an additive scale of chronic conditions; and b) six chronic disease clusters. Linear Mixed Models were employed to test hypotheses. RESULTS: Number of chronic conditions pre-pandemic was associated with pandemic levels of depression (estimate = 0.40, 95% CI: [0.37,0.44]); anxiety (estimate = 0.20, 95% CI: [0.18, 0.23]); and perceived negative impact of the pandemic (OR = 1.04, 95% CI: [1.02, 1.06]). The associations between multimorbidity and anxiety decreased during the period of the COVID-19 surveys (estimate = -0.02, 95% CI: [-0.05, -0.01]); whereas the multimorbidity association with perceived impact increased (OR = 1.03, 95% CI: [1.01, 1.05]). CONCLUSIONS: This study demonstrates that pre-pandemic multimorbidity conditions are associated with worsening mental health. CLINICAL IMPLICATIONS: Clinicians treating mental health of older adults need to consider the joint effects of multimorbidity conditions and pandemic experiences to tailor counseling and other treatment protocols.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".