Perceptions of Conflict at the Transition to Parenthood: Exploring Adult Attachment Pairings as Predictors of Emotional Flooding
Bibliographic record
Abstract
Understanding how attachment styles between partners relate to the dysregulation of emotions during couple conflict has received little attention, especially over the transition to parenthood. This research investigated how combinations of expectant couples' attachment styles jointly predict emotional flooding, which is a form of interpersonal emotion dysregulation. Using a sample of 98 mixed-gender couples residing in Canada, we used multilevel modeling to examine actor effects (e.g., one's attachment insecurity predicting their own flooding), partner effects (one's attachment insecurity predicting flooding in their partner), and interactions between partners to examine its association with emotional flooding at the third trimester of pregnancy and across early parenthood. Longitudinally, couples were followed from the third trimester to 4 years postpartum to explore how attachment pairings predicted changes in flooding across parenthood. Attachment anxiety in men predicted their own propensity to become flooded during conflict, as well as their partner's flooding. An interaction was seen at the third trimester, such that men who were avoidantly attached reported greater flooding when their partner was high in anxiety compared to low in anxiety. Finally, men's flooding was associated with greater increases over time when high avoidance in men was paired with low avoidance in women, whereas flooding showed the smallest increase when both partners reported low avoidance. Findings suggest that the fit between each partner's attachment styles can improve understanding of the emotional mechanisms experienced during conflict, especially during the often-stressful period of early parenthood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".