Differential Associations Between Depressive Symptom-Domains With Anxiety, Loneliness, and Cognition in a Sample of Community Older Chinese Adults: A Multiple Indicators Multiple Causes Approach
Bibliographic record
Abstract
Abstract Background and Objectives Depressive symptoms are common in older adults, and often co-occur with other mental health problems. However, knowledge about depressive symptom-domains and their associations with other conditions is limited. This study examined depressive symptom-domains and associations with anxiety, cognition, and loneliness. Research Design and Methods A sample of 3,795 participants aged 60 years and older were recruited from the community in Hong Kong. They were assessed for depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]), anxiety (Generalized Anxiety Disorder 7-item), loneliness (UCLA 3-item), and cognition (Montreal Cognitive Assessment 5-Minute Protocol). Summary descriptive statistics were calculated, followed by confirmatory factor analysis of PHQ-9. Multiple Indicators Multiple Causes analysis was used to examine the associations between mental health conditions in the general sample and subgroups based on depressive symptom severity. Results A 4-factor model based on the Research Domain Criteria showed the best model fit of PHQ-9 (χ2/df = 10.63, Root-Mean-Square Error of Approximation = 0.05, Comparative Fit Index = 0.96, Tucker–Lewis Index = 0.93). After adjusting for demographics, 4 depressive symptom-domains were differentially associated with anxiety, loneliness, and cognition across different depression severity groups. The Negative Valance Systems and Internalizing domain (NVS-I; guilt and self-harm) were consistently associated with anxiety (β = 0.45, 0.44) and loneliness (β = 0.11, 0.27) regardless of depression severity (at risk/mild vs moderate and more severe, respectively, all p < .001). Discussion and Implications The consistent associations between the NVS-I domain of depression with anxiety and loneliness warrant attention. Simultaneous considerations of depressive symptom-domains and symptom severity are needed for designing more personalized care. Clinical Trials Registration Number NCT03593889
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".