Breastfeeding and maternal sensitivity predict early infant temperament
Bibliographic record
Abstract
AIM: Research findings are inconclusive when it comes to whether breastfeeding is associated with the mother-infant relationship or infant temperament. We examined the association between breastfeeding at three months postpartum and infant temperament at 18 months postpartum and whether this link was affected by the mothers' anxiety and mediated by her sensitivity. METHODS: We assessed 170 mothers for breastfeeding and anxiety using the Spielberger State-Trait Anxiety Inventory (STAI) at three months postpartum, maternal sensitivity using the Ainsworth Sensitivity Scale at six months postpartum and infant temperament using the Early Childhood Behaviour Questionnaire at 18 months postpartum. RESULTS: Mothers who breastfed at three months postpartum were more sensitive in their interactions with their infants at six months postpartum, and elevated sensitivity, in turn, predicted reduced levels of negative affectivity in infant temperament at 18 months postpartum. This indirect mediation persisted after controlling for confounders (effect ab = -0.0312 [0.0208], 95% CI = -0.0884 to -0.0031). A subsequent analysis showed that the mediation through sensitivity only occurred in women experiencing higher anxiety, with a STAI score ≥33.56 at three months (ab = -0.0250 [0.0179], 95% CI = -0.0759 to -0.0013). CONCLUSION: Our results suggest that breastfeeding and maternal sensitivity may have a positive impact on the early development of infant 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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".