Social support and maternal mental health at 4 months and 1 year postpartum: analysis from the All Our Families cohort
Bibliographic record
Abstract
BACKGROUND: Low social support is consistently associated with postpartum depression. Previous studies do not always control for previous mental health and do not consider what type of support (tangible, emotional, informational or positive social interaction) is most important. The objectives are: to examine if low social support contributes to subsequent risk of depressive or anxiety symptoms and to determine which type of support is most important. METHODS: Data from the All Our Families longitudinal pregnancy cohort were used (n=3057). Outcomes were depressive or anxiety symptoms at 4 months and 1 year postpartum. Exposures were social support during pregnancy and at 4 months postpartum. Log binomial models were used to calculate risk ratios (RRs) and absolute risk differences, controlling for past mental health. RESULTS: Low total social support during pregnancy was associated with an increased risk of depressive symptoms (RR 1.50, 95% CI 1.24 to 1.82) and anxiety symptoms (RR 1.63, 95% CI 1.38 to 1.93) at 4 months postpartum. Low total social support at 4 months was associated with an increased risk of anxiety symptoms (RR 1.65, 95% CI 1.31 to 2.09) at 1 year. Absolute risk differences were largest among women with previous mental health challenges resulting in a number needed to treat of 5 for some outcomes. Emotional/informational support was the most important type of support for postpartum anxiety. CONCLUSION: Group prenatal care, prenatal education and peer support programmes have the potential to improve social support. Prenatal interventions studies are needed to confirm these findings in higher risk groups.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".