Disaster in pregnancy: midwifery continuity positively impacts infant neurodevelopment, QF2011 study
Bibliographic record
Abstract
BACKGROUND: Research shows that continuity of midwifery carer in pregnancy improves maternal and neonatal outcomes. This study examines whether midwifery group practice (MGP) care during pregnancy affects infant neurodevelopment at 6-months of age compared to women receiving standard hospital maternity care (SC) in the context of a natural disaster. METHODS: This prospective cohort study included 115 women who were affected by a sudden-onset flood during pregnancy. They received one of two models of maternity care: MGP or SC. The women's flood-related objective stress, subjective reactions, and cognitive appraisal of the disaster were assessed at recruitment into the study. At 6-months postpartum they completed the Ages and Stages Questionnaire (ASQ-3) on their infants' communication, fine and gross motor, problem solving, and personal-social skills. RESULTS: Greater maternal objective and subjective stress predicted worse infant outcomes. Even when controlling for maternal stress from the flood, infants of mothers who were in the MGP model of maternity care performed better than infants of mothers in SC on two of the five ASQ-3 domains (fine motor and problem solving) at 6-months of age. Furthermore, infants in the SC model were more likely to be identified as at risk for delayed development on these domains than infants in the MGP model of care. CONCLUSIONS: Continuity of midwifery care has positive effects on infant neurodevelopment when mothers experience disaster-related stress in pregnancy, with significantly better outcomes on two developmental domains at 6 months compared to infants whose mothers received standard hospital care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".