Health Promotion and Support Grounded in Interconnected Influences on Alcohol Use in Pregnancy
Bibliographic record
Abstract
There are a range of factors that influence alcohol use in pregnancy and create risk of fetal harm. However, limited research has articulated the multilevel nature of these influences and their entanglement. The purpose of this narrative review is to analyze the types of factors that influence alcohol use and consider what factors need to be addressed in future health promotion and intervention efforts. Six databases were searched using EBSCOhost articles published between January and December 2023 on alcohol use in pregnancy and Fetal Alcohol Spectrum Disorder (FASD) prevention. English-language articles were screened for relevance and a subset of articles exploring the prevalence, influences, and risk-factors associated with pregnancy were included for analysis. Thirty-two (n = 32) articles were included in the review and categorized into five key areas of influence on maternal alcohol use: (1) informational factors; (2) stress-related factors; (3) social determinant of health-related (SDoH) influences; (4) preconception- and prenatal-health-related factors; and (5) structural factors. Future efforts to reduce alcohol use in pregnancy should address these five categories of factors through non-judgmental, health-promoting, trauma-informed, harm-reduction-oriented, and culturally safe education, programming, and policy.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".