Screening for Alcohol Use in Pregnancy: a Review of Current Practices and Perspectives
Bibliographic record
Abstract
Global trends of increasing alcohol consumption among women of childbearing age, social acceptability of women's alcohol use, as well as recent changes in alcohol use patterns due to the COVID-19 pandemic may put many pregnancies at higher risk for prenatal alcohol exposure (PAE), which can cause fetal alcohol spectrum disorder (FASD). Therefore, screening of pregnant women for alcohol use has become more important than ever and should be a public health priority. This narrative review presents the state of the science on various existing prenatal alcohol use screening strategies, including the clinical utility of validated alcohol use screening instruments. It also discusses barriers for alcohol use screening in pregnancy, such as practitioner constraints, unplanned pregnancies, delayed access to prenatal care, and stigma associated with substance use in pregnancy, providing recommendations to address these barriers. By implementing consistent alcohol use screening, prenatal care providers have the opportunity to facilitate access to counseling and brief interventions and thus, to prevent new cases of FASD and improve maternal and child health.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".