Supporting pregnant and parenting women who use alcohol during pregnancy: A scoping review of trauma-informed approaches
Bibliographic record
Abstract
Alcohol is legalized and used for a variety of reasons, including socially or as self-medication for trauma in the absence of accessible and safe supports. Trauma-informed approaches can help address the root causes of alcohol use, as well as the stigma around women's alcohol use during pregnancy. However, it is unclear how these approaches are used in contexts where pregnant and/or parenting women access care. Our objective was to synthesize existing literature and identify promising trauma-informed approaches to working with pregnant and/or parenting women who use alcohol. A multidisciplinary team of scholars with complementary expertise worked collaboratively to conduct a rigorous scoping review. All screening, extraction, and analysis was independently conducted by at least two authors before any differences were discussed and resolved through team consensus. The Joanna Briggs Institute method was used to map existing evidence from peer-reviewed articles found in PubMed, CINAHL, PsycINFO, Social Work Abstracts, and Web of Science. Data were extracted to describe study demographics, articulate trauma-informed principles in practice, and gather practice recommendations. Thirty-six studies, mostly from the United States and Canada, were included for analysis. Studies reported on findings of trauma-informed practice in different models of care, including live-in treatment centers, case coordination/management, integrated and wraparound supports, and outreach-for pregnant women, mothers, or both. We report on how the following four principles of trauma-informed practices were applied and articulated in the included studies: (1) trauma awareness; (2) safety and trustworthiness; (3) choice, collaboration, and connection; and (4) strengths-based approach and skill building. This review advances and highlights the importance of understanding trauma and applying trauma-informed practice and principles to better support women who use alcohol to reduce the risk of alcohol-exposed pregnancies. Relationships and trust are central to trauma-informed care. Moreover, when applying trauma-informed practices with pregnant and parenting women who use alcohol, we must consider the unique stigma attached to alcohol use.
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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 | 0.043 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.030 | 0.029 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".