Interventions for reducing fear of childbirth: A systematic review and meta-analysis of clinical trials
Bibliographic record
Abstract
Introduction Fear of childbirth (FOC) is a problematic mental health issue during pregnancy and postpartum period. It is essential to identify the most effective approaches to reduce the problem. Objective Using meta-analyses, we aimed to examine the most effective intervention for reduction of FOC. Method We searched the Cochran central register of controlled trials, PubMed, Embase and PsycINFO databases since inception till January 2016 without any language restriction. The reference lists of all included studies were checked for additional published reports and citations of unpublished research. We included randomised control trials and quasi-randomised control trials comparing interventions for treatment of FOC. Two review authors independently assessed trial quality and extracted data. The standardized mean differences (SMD) were pooled using random and fixed effect model. The heterogeneity was determined using the Cochran's test and I2 index and was further explored in meta-regression model and subgroup analyses. Egger's regression and funnel plot were used for assessing publication bias. Results Ten studies totalling 3984 participants were included from two quasi-experimental and eight randomised clinical trials. Eight studies investigated education and two studies investigated hypnosis-based intervention. The pooled SMD of FOC for the education intervention and hypnosis group in comparison with control group were -0.46 (95% CI-0.73 to -0.19) and -0.22 (95% CI-0.34 to -0.10), respectively. Conclusion Interventions were effective on reducing FOC; however educational interventions reduced FOC twice as high as hypnosis. This result highlights the roll of antenatal education in enhancing childbirth expectations and experiences to enhance maternal and neonatal well-being. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.036 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".