Infant negativity moderates trajectories of maternal emotion across pregnancy and the peripartum period
Bibliographic record
Abstract
Background: Although the effects of maternal behavior on the development of child emotion characteristics is relatively well-established, effects of infant characteristics on maternal emotion development is less well known. This gap in knowledge persists despite repeated calls for including child-to-mother effects in studies of emotion. We tested the theory-based postulate that infant temperamental negativity moderates longitudinal trajectories of mothers' perinatal symptoms of anxiety and depression. Method: Participants were 92 pregnant community women who enrolled in a longitudinal study of maternal mental health; symptoms of anxiety and depression were assessed during the second and third trimesters of pregnancy and again at infant age 4 months. A multimethod assessment of infants' temperament-based negative reactivity was conducted at infant age 4 months. Results: Maternal symptoms of anxiety showed smaller postnatal declines when levels of infant negativity were high. Negative reactivity, assessed via maternal report of infant behavior, was related to smaller postnatal declines in maternal anxiety, while infant negative reactivity, at the level of neuroendocrine function, was largely unrelated to longitudinal changes in maternal anxiety symptoms. Infant negativity was related to early levels, but largely unrelated to trajectories of maternal symptoms of depression. Limitations: Limitations of this work include a relatively small and low-risk sample size, the inability to isolate environmental effects, and a nonexperimental design that precludes causal inference. Conclusions: Findings suggest that levels of infant negativity are associated with differences in the degree of change in maternal anxiety symptoms across the perinatal period.
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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 | 0.004 |
| 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 | 0.000 |
| 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".