Fetal Alcohol Spectrum Disorder (FASD) Prevention among AI/AN Women in San Diego County
Bibliographic record
Abstract
Prenatal alcohol exposure results in a variety of diverse conditions known collectively as Fetal Alcohol Spectrum Disorders (FASD). FASDs range from mild to severe and present a burden to the affected individual themselves as well as to their family and community. In the present study, we explore risk factors for vulnerability to prenatal alcohol exposure among a sample of 263 AIAN women of childbearing age in Southern California. In addition we evaluate the outcome of a culturally tailored web-based Screening, Brief Intervention, and Referral to Treatment (SBIRT) intervention over a 6-month follow-up period. Our findings are that a subset of our participants, roughly a third, was vulnerable to alcohol-exposed pregnancy. Approximately half of participants reported not drinking alcohol but those who did drink tended to drink in a heavy episodic or "binge" pattern. A quarter of participants used no form of birth control (including abstinence) while less than a quarter used highly effective contraception. Risk/protective factors included knowledge regarding the risks associated with alcohol consumption, religiosity, and the perception of cultural norms. Participation in assessment alone, without exposure to the intervention, was sufficient to result in significant positive behavioral change (decreased drinks per week, p<0.001; frequency of binge drinking episodes, p=0.017; risk of alcohol-exposed pregnancy, p<0.001). There was no difference between treatment groups. Depression was associated with risk factors for vulnerability to alcohol- exposed pregnancy. Depressed women received additional benefit from the intervention, experiencing greater reduction in risky behavior than women not identified as depressed. Study results support the incorporation into future FASD prevention interventions of information regarding FASD and the risks of alcohol consumption, efforts to support healthy and to shift away from unhealthy cultural norms, contraceptive counseling, screening for depression, and personalized interventions for women identified as depressed. Findings that assessment alone reduces risky drinking and vulnerability to alcohol-exposed pregnancy indicate a value to assessment even when logistic limitations prevent the provision of an individualized intervention
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".