ANXIETY AND DEPRESSION DURING PREGNANCY AND TEMPERAMENT IN EARLY INFANCY: FINDINGS FROM A MULTI‐ETHNIC, ASIAN, PROSPECTIVE BIRTH COHORT STUDY
Bibliographic record
Abstract
Maternal antenatal mood is associated with negative infant temperament. This link has not been substantiated in Asian populations. We evaluated the association between antenatal maternal mood and infant temperament among Asian mother-infant pairs. Antenatal maternal depression and anxiety were assessed using the Edinburgh Postnatal Depression Scale (J. Cox, J. Holden, & R. Sagovsky, 1987) and the State-Trait Anxiety Inventory (C. Spielberger, R. Gorsuch, R. Lushene, P. Vagg, & G. Jacobs, 1983), respectively, at 26 weeks of pregnancy and 3 months' postnatally. Infant temperament was evaluated with the Early Infant Temperament Questionnaire (B. Medoff-Cooper, W.B. Carey, & S.C. McDevitt, 1993) at 3 months. Factor analysis was performed to extract culturally relevant categories of temperamental traits. Linear regression was performed to examine the influences of antenatal maternal mood on the factor-model-derived infant temperament. Of the 609 mothers, 11% met risk criteria for depression, 17% for state-anxiety, and 19% for trait-anxiety during pregnancy. Factor analysis yielded three infant temperament factors: Emotionality and Attentional Regulation, Sensory Reactivity, and Regularity and Motor Expression, Cronbach's αs = 0.613, 0.712, and 0.752, respectively. Maternal antenatal state-anxiety, p < .001, and trait anxiety, p = .005, were associated with negative emotionality and poor attentional regulation, especially among Chinese, whereas depression was not, p = .090. There was an association between maternal antenatal anxiety and negative infant temperamental traits in this Asian sample.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".