Negative emotionality as a candidate mediating mechanism linking prenatal maternal mood problems and offspring internalizing behaviour
Bibliographic record
Abstract
Negative emotionality (NE) was evaluated as a candidate mechanism linking prenatal maternal affective symptoms and offspring internalizing problems during the preschool/early school age period. The participants were 335 mother-infant dyads from the Maternal Adversity, Vulnerability and Neurodevelopment project. A Confirmatory Bifactor Analysis (CFA) based on self-report measures of prenatal depression and pregnancy-specific anxiety generated a general factor representing overlapping symptoms of prenatal maternal psychopathology and four distinct symptom factors representing pregnancy-specific anxiety, negative affect, anhedonia and somatization. NE was rated by the mother at 18 and 36 months. CFA based on measures of father, mother, child-rated measures and a semistructured interview generated a general internalizing factor representing overlapping symptoms of child internalizing psychopathology accounting for the unique contribution of each informant. Path analyses revealed significant relationships among the general maternal affective psychopathology, the pregnancy- specific anxiety, and the child internalizing factors. Child NE mediated only the relationship between pregnancy-specific anxiety and the child internalizing factors. We highlighted the conditions in which prenatal maternal affective symptoms predicts child internalizing problems emerging early in development, including consideration of different mechanistic pathways for different maternal prenatal symptom presentations and child temperament.
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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 | 0.002 |
| 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 | 0.000 | 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".