Alcohol exposure among pregnant women in sub-saharan Africa: a systematic review.
Bibliographic record
Abstract
BACKGROUND: The prevalence of general alcohol use in many countries of sub-Saharan Africa (SSA) is high. However, research examining alcohol use in among pregnant women within this population is limited. A review of the current status of research examining the prevalence of alcohol exposed pregnancies (AEP) is required to inform future research aiming to decrease this occurrence and its subsequent socio-economic complications. OBJECTIVE: The primary objective was to identify all published papers estimating prevalence and risk-factors of alcohol use among pregnant women in SSA. A secondary objective was to determine changes in alcohol use following pregnancy recognition. METHODS: PubMed/Medline, Embase, IPA, CINAHL were systematically searched using MeSH terms and keywords from inception date to March 2013. Studies from SSA reporting prevalence of alcohol use among pregnant women were included. RESULTS: Twelve studies were identified. Studies varied significantly according to design and study population. Prevalence of alcohol use during pregnancy ranged from 2.2%-87%. The most important risk-factors for alcohol use included tobacco use, partner violence, urban living, and having a male partner who drank alcohol. Only three studies examined changes in alcohol use prior to and following pregnancy recognition with absolute reductions of between 9% and 15%. CONCLUSIONS: Although the burden of alcohol use during pregnancy is likely a significant problem, limited data currently exist for the majority of SSA countries. Furthermore, significant variation likely exists within various populations. Further research is required to explore alcohol use in pregnancy. Strategies to decrease AEP must be developed and implemented in standard pre-natal care.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".