Relationships between mental health and diet during pregnancy and birth outcomes in a lower‐middle income country: “Healthy mothers, healthy communities” study in Vanuatu
Bibliographic record
Abstract
Poor maternal mental health during pregnancy is associated with adverse birth outcomes, including lower birthweight and gestational age. However, few studies assess both mental health and diet, which might have interactive effects. Furthermore, most studies are in high-income countries, though patterns might differ in low- and middle-income countries (LMICs). OBJECTIVES: To analyze relationships between mental health and diet during pregnancy with birth outcomes in Vanuatu, a lower-middle income country. METHODS: We assessed negative emotional symptoms of depression, anxiety, and stress (referred to as "distress") and dietary diversity during pregnancy, and infant weight and gestational age at birth, among 187 women. We used multivariate linear regression to analyze independent and interactive relationships between distress, dietary diversity, and birth outcomes, controlling for sociodemographic and maternal health covariates. RESULTS: There were no direct linear relationships between dietary diversity or distress with infant birthweight or gestational age, and no curvilinear relationships between distress and infant outcomes. We observed interactive relationships between distress and dietary diversity on birthweight, explaining 2.1% of unique variance (P = .024). High levels of distress predicted lower birthweights among women with low dietary diversity. These relationships were not evident among women with moderate or high dietary diversity. CONCLUSIONS: Relationships between mental health and diet might underlie inconsistencies in past studies of prenatal mental health and birthweight. Results highlight the importance of maternal mental health on birthweight in LMICs. Interactive relationships between mental health and diet might ultimately point to new intervention pathways to address the persistent problem of low birthweight in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".