Trauma Transmission among Parent Survivors of Cumulative Childhood Interpersonal Trauma: The Protective Role of Partner Support
Bibliographic record
Abstract
BACKGROUND: The intergenerational transmission of cumulative childhood interpersonal trauma (CCIT) is well established. While the protective role of relational factors, including partner support, is well recognized, few studies examined this question using dyadic designs, representing an important gap in the literature. OBJECTIVE: This study tested whether provided and received partner support (i.e., the support one provides to their partner and the support one receives from their partner) moderated the association between parents' CCIT and their own and their partner's child abuse potential (CAP). PARTICIPANTS AND SETTING: Participants were 607 heterosexual couples (N = 1214 parents) of toddlers recruited from a community-based longitudinal study in Canada. METHODS: Parents completed validated self-report measures of CCIT, partner support, and CAP. Analyses were conducted using the Actor-Partner Interdependence Model (APIM), accounting for non-independence of dyadic data and controlling for stressful life events. RESULTS: Parents' CCIT was positively associated with their own CAP (β = 0.178, p < .001). Received partner support was negatively associated with CAP (β = -0.288, p < .001) and moderated the CCIT-CAP link both within (β = -0.094, p = .008) and across partners (β = -0.075, p = .026). The association between CCIT and CAP became nonsignificant at high levels of received support. The final model explained 30% of CAP variance and fit the data well. CONCLUSIONS: Supportive couple dynamics mitigate the intergenerational transmission of trauma. Enhancing perceived partner support represents a promising avenue for preventing child maltreatment among trauma-exposed parents.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".