Epigenetics of Trauma Transmission and Fetal Alcohol Spectrum Disorder: What Does the Evidence Support?
Bibliographic record
Abstract
Fetal alcohol spectrum disorder (FASD) results from teratogenic impacts of alcohol consumption during pregnancy. Trauma and prenatal alcohol exposure (PAE) can both cause neurodevelopmental impairment, and it has been proposed that FASD can amplify effects of trauma. Certain PAE and trauma effects are mediated via epigenetic mechanisms. The objective of this review is to present the current evidence for epigenetics in trauma transmission as it relates to FASD, to help bridge a potential knowledge gap for social workers and related health professionals. We include a primer on epigenetic mechanisms and inheritance, followed by a summary of the current biomedical evidence supporting intergenerational and transgenerational epigenetic transmission of trauma, its relevance to FASD, the intersection with social transmission, and finally the application to social work. We propose potential models of transmission, considering where social and epigenetic pathways may intersect and/or compound across generations. Overall, we aim to provide a better understanding of epigenetic-trauma transmission for its application to health professions, in particular which beliefs are (and are not) evidence-based. We discuss the lack of research and challenges of studying epigenetic transmission in humans and identify the need for public health interventions and best practices that are based on the current evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".