Social support and prenatal mental health problems: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Pregnancy is a time of profound physical and emotional change as well as an increased risk of mental health problems. Providing social support is vital to reduce such risk. Objectives This systematic review and meta-analysis aimed at examining the relationship between social support and depression, anxiety and self-harm during pregnancy. Methods We searched observational studies from PubMed, Psych Info, MIDIRS, SCOPUS, and CINAHL databases. The Newcastle-Ottawa Scale tool was used for quality appraisal. The Q and the I² statistics were used to evaluate heterogeneity. A random-effects model was used to pool estimates. Publication bias was assessed using a funnel plot and Egger’s regression test and adjusted using trim and Fill analysis. All the analysis was conducted using STATA. Results Sixty-seven studies with 64,449 pregnant women were part of the current review. Of the total 67 studies, 22 and 45 studies were included in the narrative analysis and meta-analysis, respectively. From the studies included in the narrative analysis, 20(91%) of them reported a significant association between social support and the risk of mental health problems (i.e. depression, anxiety, and self-harm). After adjusting for publication bias, the results of the random-effect model revealed low social support was significantly associated with antenatal depression (AOR: 1.18, 95% CI: 1.01, 1.41) and antenatal anxiety (AOR: 1.97, 95% CI: 1.34, 2.92). Conclusions Low social support was significantly associated with depression, anxiety, and self-harm during pregnancy. Policy-makers and those working on maternity care should consider the development of targeted social support programs to help reduce mental health problems amongst pregnant women. Disclosure No significant relationships.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".