“The Problem Is that We Hear a Bit of Everything…”: A Qualitative Systematic Review of Factors Associated with Alcohol Use, Reduction, and Abstinence in Pregnancy
Bibliographic record
Abstract
Understanding the factors that contribute to women’s alcohol use in pregnancy is critical to supporting women’s health and wellness and preventing Fetal Alcohol Spectrum Disorder. A systematic review of qualitative studies involving pregnant and recently postpartum women was undertaken to understand the barriers and facilitators that influence alcohol use in pregnancy (PROSPERO: CRD42018098831). Twenty-seven (n = 27) articles were identified through EMBASE, CINAHL, PsycINFO, PubMed and Web of Science. The included articles were thematically analyzed using NVivo12. The analysis was informed by Canada’s Action Framework for Building an Inclusive Health System to articulate the ways in which stigma and related barriers are enacted at the individual, interpersonal, institutional and population levels. Five themes impacting women’s alcohol use, abstention and reduction were identified: (1) social relationships and norms; (2) stigma; (3) trauma and other stressors; (4) alcohol information and messaging; and (5) access to trusted equitable care and essential resources. The impact of structural and systemic factors on prenatal alcohol use was largely absent in the included studies, instead focusing on individual choice. This silence risks perpetuating stigma and highlights the criticality of addressing intersecting structural and systemic factors in supporting maternal and fetal 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 | 0.034 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".