The relationship between perceived social support and fear of childbirth in pregnant women: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite being a natural process, childbirth often evokes fear due to its unpredictable nature. This fear can lead to significant emotional distress and various physical and mental health complications. While social support from family, friends, and partners is thought to reduce fear, its effectiveness remains unclear. This systematic review and meta-analysis aims to determine the relationship between fear of childbirth and various sources of social support, including overall support, support from family, friends, and partners. METHODS: A systematic search was conducted across PubMed, Web of Science, Cochrane, Scopus, SID, and Google Scholar for relevant studies published through November 2024. Study quality was assessed using the Newcastle-Ottawa Scale. Subgroup analyses were performed based on study quality. To determine result robustness, two separate sensitivity analyses were carried out: one in which individual studies were sequentially removed, and another where studies using different assessment tools were excluded. Finally, the influence of maternal age, gestational age, multiparity, and pregnancy planning on outcomes was examined through meta-regression analysis. RESULTS: From 1,542 screened studies, 17 were included (n = 5,535 women). Meta-analysis revealed significant inverse correlations between fear of childbirth and both perceived social support (r = -0.23, 95% CI -0.39 to -0.05, 16 studies, 5,435 women; p = 0.01; random-effects model) and partner support (r = -0.29, 95% CI -0.46 to -0.09, 5 studies, 1,254 women; p < 0.01; random-effects model). No significant associations emerged for family (r = -0.12, 95% CI -0.26 to 0.02, 3 studies, 530 women; p = 0.10; random-effects model) or friend support (r = -0.05, 95% CI -0.14 to 0.03, 3 studies, 530 women; p = 0.22; fixed-effects model). Results varied significantly by study quality (p < 0.001) but were unaffected by maternal characteristics in meta-regression. Sensitivity analyses confirmed result stability. CONCLUSION: This meta-analysis suggests that greater social support, particularly from partners, can help alleviate fear of childbirth. However, support from friends and family did not show a clear link to reduced fear. Due to limitations in the quality of the studies reviewed, further high-quality research is needed to draw definitive conclusions.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.003 | 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".