Social support buffers the impact of pregnancy stress on perceptions of parent–infant closeness during the COVID‐19 pandemic
Bibliographic record
Abstract
Pregnant individuals and parents have experienced elevated mental health problems and stress during COVID-19. Stress during pregnancy can be harmful to the fetus and detrimental to the parent-child relationship. However, social support is known to act as a protective factor, buffering against the adverse effects of stress. The present study examined whether (1) prenatal stress during COVID-19 was associated with parent-infant closeness at 6 months postpartum, and (2) social support moderated the effect of prenatal stress on the parent-infant relationship. In total, 181 participants completed questionnaires during pregnancy and at 6 months postpartum. A hierarchical linear regression analysis was conducted to assess whether social support moderated the effect of stress during pregnancy on parent-infant closeness at 6 months postpartum. Results indicated a significant interaction between prenatal stress and social support on parents' perceptions of closeness with their infants at 6 months postpartum (β = .805, p = .029); parents who experienced high prenatal stress with high social support reported greater parent-infant closeness, compared to those who reported high levels of stress and low social support. Findings underscore the importance of social support in protecting the parent-infant relationship, particularly in times of high stress, such as during the COVID-19 pandemic.
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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 | 0.005 |
| 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) | 0.001 | 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".