Bibliographic record
Abstract
Background: Social support, in the form of emotional, informational and tangible resources provided by friends and family is beneficial for health. Social support in pregnancy and the postpartum period is also thought to improve birth outcomes and maternal mental health. However, questions remain as to what type of support is important and for which outcomes. In addition, little is known about patterns of support over time. Methods: A systematic review was conducted to determine the association between low social support and preterm birth. Data from the All Our Families cohort (n=3200) was used for the second two projects This cohort recruited women in pregnancy and followed them to 1 year postpartum, measuring demographic, psychosocial and birth outcome information. Multivariable binomial regression was used to estimate the impact of social support during pregnancy and in the early postpartum period on subsequent mental health symptoms. Group based trajectory modeling was used to determine patterns of support from pregnancy to four months postpartum, followed by multinomial regression to determine characteristics associated with different patterns of support. Results: The systematic review found no direct association between social support and preterm birth, however low social support was associated with preterm birth among women experiencing high stress. The second analysis revealed elevated risk of subsequent depression and anxiety symptoms among women with low support, across various levels of previous mental health risk. Finally, the trajectories analysis showed stable support among 98% of women. Stable high support (60% of women) was associated with higher income. Conclusion: Social support can impact both birth outcomes and maternal mental health, and is relatively stable for most women during pregnancy and postpartum. Interventions to improve support will have a larger absolute benefit for women who may be vulnerable due to previous mental health challenges. More research is needed to understand how to influence conditions that will allow women to develop and maintain strong support networks.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".