Postpartum Depressive Symptoms as a Mediator Between Intimate Partner Violence During Pregnancy and Maternal-Infant Bonding in Japan
Bibliographic record
Abstract
Studies show that experiencing intimate partner violence (IPV) during pregnancy is related to poor maternal–infant bonding. However, the mechanisms underlying this relationship are unclear. This article aims to examine whether maternal postpartum depressive (PPD) symptoms mediate the association between pregnancy IPV and maternal–infant bonding, and whether the relationship differs by maternal–infant bonding subscales—lack of affection, anger/rejection. A survey was conducted among women who participated in a postpartum health check-up program in Aichi prefecture, Japan ( N = 6,590) in 2012. We examined whether experiences of emotional and physical IPV were related to maternal–infant bonding and whether PPD symptoms mediated this relationship. Path analysis showed that emotional and physical IPV were associated with PPD symptoms, and PPD symptoms predicted poor bonding. The total effect of emotional IPV on poor bonding was significant, showing a marginally significant direct effect and statistically significant indirect effect. The total effect of physical IPV on poor bonding was not statistically significant. Emotional IPV was significantly associated with both lack of affection and anger/rejection bonding subscales, which were similarly mediated by PPD symptoms. Findings revealed a modest indirect association between IPV, emotional IPV in particular, and poor maternal–infant bonding, which was mediated by PPD symptoms. While prevention of IPV is the ultimate goal, the treatment of PPD symptoms among women who experience IPV during pregnancy may improve maternal–infant bonding and mitigate cross-generational effects of IPV. Identifying opportunities for detection of IPV and PPD symptoms, as well as prevention and early intervention, may improve maternal–infant bonding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".