To what extent do social support and coping strategies mediate the relation between childhood maltreatment and major depressive disorder: A longitudinal community-based cohort
Bibliographic record
Abstract
This study aimed to articulate the roles of social support and coping strategies in the relation between childhood maltreatment (CM) and subsequent major depressive disorder (MDD) with a comprehensive exploration of potential factors in a longitudinal community-based cohort. Parallel and serial mediation analyses were applied to estimate the direct effect (DE) (from CM to MDD) and indirect effects (from CM to MDD through social support and coping strategies, simultaneously and sequentially). Sociodemographic characteristics and genetic predispositions of MDD were considered in the modeling process. A total of 902 participants were included in the analyses. CM was significantly associated with MDD (DE coefficient (β) = 0.015, 95% confidence interval (CI) = 0.002∼0.028). This relation was partially mediated by social support (indirect β = 0.004, 95% CI = 0.0001∼0.008) and negative coping (indirect β = 0.013, 95% CI = 0.008∼0.020), respectively. Social support, positive coping, and negative coping also influenced each other and collectively mediated the association between CM and MDD. This study provides robust evidence that although CM has a detrimental effect on later-on MDD, social support and coping strategies could be viable solutions to minimize the risk of MDD. Intervention and prevention programs should primarily focus on weakening negative coping strategies, then strengthening social support and positive coping strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".