Association between intimate partner violence during pregnancy and risk of preterm birth
Bibliographic record
Abstract
BACKGROUND: Preterm birth is a risk factor for child survival in both the short and long term. In Zimbabwe, the prevalence of preterm birth is rising, and there are growing concerns about the adverse consequences. This study explored the association between intimate partner violence (IPV) during pregnancy and preterm birth in Zimbabwe. METHODS: Using data from the 2015 Zimbabwe Demographic and Health Survey, we applied propensity score matching to estimate the effect of IPV during pregnancy on preterm birth among women of reproductive age (15-49 years). A total of 4833 pregnant women who gave birth during the five years preceding the survey were analysed. RESULTS: We successfully matched 79 women who were exposed to IPV during pregnancy to 372 unexposed during pregnancy. Using the matched sample, the probability of preterm delivery was significantly higher among women who were exposed to IPV during pregnancy than those who were not exposed. The findings showed that 7 out of 79 (8.9%) of women exposed to IPV during pregnancy experienced preterm delivery, and 11 out of 372 (3.0%) of those who were not exposed to IPV during pregnancy experienced preterm delivery. In the urban areas, those exposed to IPV during pregnancy were almost five times more likely to experience preterm delivery (OR = 4.8, 95% CI 2.0-11.6), but the association was not significantly different among women in rural areas. CONCLUSION: The findings showed that women exposed to IPV during pregnancy were at increased risk of preterm birth. Some of the risk factors associated with IPV were urban residence, low economic status and unemployment. Effective policies and programmes are required to address the issue of IPV in Zimbabwe.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".