Emotion regulation during pregnancy: A pathway from maternal childhood maltreatment to perinatal mental health
Bibliographic record
Abstract
The perinatal period is a critical time during which the effects of maternal early life adversity can manifest for both the pregnant individual and their child. Early life adversity in the form of childhood maltreatment (CM) - encompassing abuse, neglect, and witnessing interpersonal violence - is widely prevalent. History of CM is linked to elevated perinatal mental health problems. Emotion regulation difficulties represent one potential mechanism through which CM can contribute to elevated risk for perinatal mental health. As such, this longitudinal study explored how CM influences maternal mental health outcomes through emotion dysregulation. A total of 128 participants from Nova Scotia, Canada, were recruited during pregnancy and completed study sessions during their third trimester and at 2 weeks postpartum. Participants reported experiences of CM and difficulties with emotion regulation during pregnancy, and symptoms of depression and anxiety at both timepoints. Mediation path analyses indicate that (1) greater CM was associated with increased difficulties in emotion regulation during pregnancy, (2) emotion dysregulation during pregnancy was linked to higher levels of anxiety and depression symptoms in pregnancy and the postpartum, and (3) emotion dysregulation mediated the association between CM and perinatal mental health outcomes. The findings highlight emotion dysregulation as one pathway through which early life adversity impacts maternal mental health across the perinatal period. Future research and clinical efforts should prioritize the development of accessible interventions aimed at supporting pregnant individuals with a history of CM, specifically in enhancing their emotion regulation abilities.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".