To what extent do social support and mastery mediate the association between childhood maltreatment and depression? A sequential causal mediation analysis
Bibliographic record
Abstract
AIMS: This study aimed to examine the independent roles of various childhood maltreatment (CM) subtypes in the development of depression; quantify the joint mediation effect of social support and mastery in the association between subtypes of CM and depression and examine the additional contribution of mastery beyond the effect that is operating through social support to this relationship. METHODS: Data analysed were from the Zone d'Épidémiologie Psychiatrique du Sud-Ouest de Montréal, an ongoing longitudinal population-based study. In total, 1351 participants with complete information on the studied variables were included. The propensity score matching and inverse-probability weighted regression adjustment estimation methods were used to minimise the potential confounding in the relationship between CM and major depression. We then used inverse odds ratio-weighted estimation to estimate the direct effects of maltreatment and indirect effects of social support and mastery. RESULTS: We found that exposures to all maltreatment subtypes increased the risk of subsequent depression. The joint mediating effect of social support and mastery explained 37.63-46.97% of the association between different maltreatment subtypes and depression. The contribution of these two mediators differed by maltreatment subtypes, with social support being the major contributor to the mediating effect. CONCLUSIONS: The findings of the study not only provide scientific evidence on the importance of psychosocial attributes in the development of major depression but also suggest that prevention and invention strategies should focus on these psychosocial attributes to effectively break the vicious cycle of CM on major depression.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".