Attentional avoidance of emotional stimuli in postpartum women with childhood history of maltreatment and difficulties with emotion regulation.
Bibliographic record
Abstract
Child abuse and neglect can lead to difficulties regulating responses to threatening and emotional situations. Exposure to childhood maltreatment has been linked to conflicting findings of both attention biases toward and away from threat-related information. The aim of the current study was to investigate whether emotion regulation moderated the association between history of childhood maltreatment and attention bias in a sample of postpartum women. One hundred forty women participated in the study at 7 months postpartum. Selective attention to both negative emotional and attachment-related negative emotional words was assessed using the Emotional Stroop task. The latent variable of difficulties with emotion regulation was found to significantly moderate the association between history of childhood maltreatment and attention bias to both negative emotional (β = -0.15, t = -2.04, p < .05) and attachment-related negative emotional stimuli (β = -0.16, t = -2.98, p < .05). In women with higher childhood trauma scores, those with greater emotion regulation difficulties displayed decreased attention to negative emotional and attachment-related emotional stimuli. In contrast, women reporting higher exposure to childhood maltreatment with greater emotion regulation capacity, displayed increased attention toward negative emotional and attachment-related emotional stimuli. This study provides evidence for attentional avoidance of emotional material in postpartum women with greater experiences of maltreatment and difficulties with emotion regulation. As the postpartum period has significant implications for maternal well-being and infant development, these findings are discussed in terms of maternal responsiveness, sensitivity to threat, and the intergenerational transmission of risk. (PsycINFO Database Record
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".