The Effectiveness of Prenatal Care Programs on Reducing Preterm Birth in Socioeconomically Disadvantaged Women
Bibliographic record
Abstract
Background: Preterm Birth (PTB) is one of the leading causes of infant morbidity and mortality. Prenatal care is an effective way to improve pregnancy outcomes but there is limited evidence of effective interventions to improve perinatal outcomes in disadvantaged pregnant women. This review was conducted with the aim to assess the effectiveness of prenatal care programs in reducing PTB in socioeconomically disadvantaged women. Materials and Methods: We searched the Scopus, PubMed, Web of Science, and Cochrane Library databases from January 1, 1990 to August 31, 2021. The inclusion criteria included clinical trials and cohort studies focusing on prenatal care in deprived women with the primary outcome of PTB (< 37 weeks). Risk of bias was assessed using the Cochrane Collaboration's tool for assessing risk of bias and the Newcastle–Ottawa Scale. Heterogeneity was evaluated using the Q test and I 2 statistics. The pooled odds ratio was calculated using random-effects models. Results: In total, 14 articles covering 22,526 women were included in the meta-analysis. Interventions/exposures included group prenatal care, home visits, psychosomatic programs, integrated intervention on socio-behavioral risk factors, and behavioral intervention through education, social support, joint management, and multidisciplinary care. The pooled results showed that all types of interventions/exposure were associated with a reduction in the risk of PTB [OR = 0.86; 95% confidence interval: (0.64, 1.16); I 2 = 79.42%]. Conclusions: Alternative models of prenatal care reduce PTB in socioeconomically disadvantaged women compared with standard care. The limited number of studies may affect the power of this study.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".