Social activity as a mediator between childhood adversity and depressive symptoms in middle-aged and older Chinese adults
Bibliographic record
Abstract
Background: Adverse childhood experiences (ACEs) are associated with an increased risk of depressive symptoms (DS) in older adults. This study investigated the role of social activity in mediating the relationship between ACEs and DS among middle-aged and older Chinese adults. Methods: Data were derived from the China Health and Retirement Longitudinal Study (CHARLS). The discovery dataset included 10,164 participants from 2018, matched with life history data from 2014, while the replication dataset comprised 8,899 participants from 2020. DS was measured using the Center for Epidemiologic Studies Depression Scale (CES-D). Multiple linear regression and mediation analysis were conducted. Results: In the discovery dataset, ACEs were positively correlated with DS (r = 0.17, p < 0.001), while social activity was negatively correlated with both DS (r = -0.11, p < 0.001) and ACEs (r = -0.03, p = 0.01). Mediation analysis indicated that ACEs significantly predicted DS (estimate = 0.51, 95% CI 0.43 to 0.60), and social activity partially mediated this relationship (estimate = -0.01, bootstrap 95% CI -0.01 to -0.001), particularly among middle-aged adults (indirect effect estimate = -0.01, bootstrap 95% CI -0.01 to -0.001). Additionally, social activity notably mediated the relationship between childhood violence exposure and DS (estimate = -0.02, bootstrap 95% CI -0.04 to -0.003). These results were robustly validated through replication analysis, reinforcing the reliability of our conclusions. Conclusions: Social activity mediates the relationship between ACEs and DS, highlighting the importance of social engagement to reduce depression risk 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".