Preterm Birth and Social Support during Pregnancy: a Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Additional social support is often recommended for women during the prenatal period to optimise birth outcomes, specifically to avoid preterm birth. Social support is thought to act in one of two ways: by reducing stress and anxiety, or by providing coping mechanisms for women with high stress. However, evidence in this area is mixed. The purpose of this meta-analysis is to determine if low levels of social support are associated with an increased risk for preterm birth. METHODS: Six databases were searched for randomised control trials and cohort studies regarding social support and preterm birth with no limits set on date or language. Inclusion criteria included the use of a validated instrument to measure social support, and studies conducted in high-income or high-middle-income countries. RESULTS: There were 3467 records retrieved, 16 of which met the inclusion criteria. Eight studies (n = 14 630 subjects) demonstrated a pooled odds ratio (OR) of 1.22 (95% CI 0.84, 1.76) for preterm birth in women with low social support compared with high social support. Among women with high stress levels, two studies (n = 6374 subjects) yielded a pooled OR of 1.52 (95% CI 1.18, 1.97). The results of six studies could not be pooled due to incompatibility of outcome measures. CONCLUSIONS: There is no evidence for a direct association between social support and preterm birth. Social support, however, may provide a buffering mechanism between stress and preterm birth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".