Examining the Mediating Effects of Dissociation in the Association Between Maternal Histories of Maltreatment and Mother-Infant Bonding
Bibliographic record
Abstract
Child maltreatment (CM) is a major global concern. Research has shown that CM may impact early attachment. However, few studies have examined factors related to the perinatal period, such as symptoms of dissociation during pregnancy and maternal histories of experiencing CM. Given the potential implications for early intervention and prevention, this study aimed to examine if prenatal dissociation mediates the association between maternal histories of CM (total and subtypes) and early mother-infant bonding at three months postpartum using an at-risk sample of younger mothers with high rates of CM. The sample included women, between 18 to 29 years old, who completed online surveys during pregnancy (Time 1 n = 85) and postpartum (Time 2 n = 57). Mediation models were run using SPSS PROCESS. The results highlighted that with the CM total score, there was a significant indirect effect from CM to early bonding through prenatal maternal dissociation. This model explained 19.89% of the variance of early bonding. The models examining the four CM subtypes individually (neglect, physical abuse, emotional maltreatment, and sexual abuse) yielded similar results. These results have implications for practitioners, such as targeting maternal histories of CM, dissociation, and early bonding using trauma-informed care practices during the perinatal period.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".