Social Support and Positive Coping Skills as Mediators Buffering the Impact of Childhood Maltreatment on Psychological Distress and Positive Mental Health in Adulthood: Analysis of a National Population-Based Sample
Bibliographic record
Abstract
There is little research on how childhood maltreatment influences the use of resilience mechanisms that are key to mental health outcomes in the face of adversity. We assessed the mediating roles of social support and positive coping skills in the relationships between childhood maltreatment and both psychological distress and positive mental health. We analyzed data from a national population survey, the 2012 Canadian Community Health Survey-Mental Health (CCHS-MH 2012, n = 25,113). Confirmatory factor analysis and structural equation modeling were used to model the relationships between childhood maltreatment, social support, and positive coping skills and their direct and mediated effect on psychological distress and positive mental health. Childhood maltreatment was found to be negatively associated with social support, positive coping skills, and positive mental health but positively associated with psychological distress. Social support and positive coping skills predicted higher rates of positive mental health but lower rates of psychological distress. Social support and positive coping skills partially mediated the negative consequences of childhood maltreatment on mental health outcomes. Surprisingly, no sex differences were observed among these associations. This research clearly demonstrates that social support and positive coping skills can mediate the negative impact of childhood maltreatment on mental health.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".