The Psychiatric Morbidity of Women Who Give Birth to Children with Fetal Alcohol Spectrum Disorder (FASD): Results of the Manitoba Mothers and FASD Study
Bibliographic record
Abstract
OBJECTIVE: To investigate differences in physician-diagnosed psychiatric disorders between women who gave birth to children with a fetal alcohol spectrum disorder (FASD) diagnosis (study group) compared to women who gave birth to children without FASD (comparison group). METHODS: We linked population-level health and social services data to clinical data on FASD diagnoses to identify study group ( n = 702) and comparison group ( n = 2097) women matched 1:3 on date of birth of index child, region of residence, and socioeconomic status. Regression modeling produced relative rates (RRs) for outcomes. RESULTS: Mothers who gave birth to children with FASD had higher adjusted rates of substance use disorder (RR, 12.65; 95% confidence interval [CI], 8.99-17.80), personality disorder (RR, 12.93; 95% CI, 4.88-34.22), and mood and anxiety disorders (RR, 1.75; 95% CI, 1.49-2.07) before the pregnancy of the child. These mothers also had higher adjusted rates of maternal psychological distress during pregnancy (RR, 5.35; 95% CI, 4.58-6.35) and higher rates of postpartum psychological distress (RR, 1.71; 95% CI, 1.53-1.90). These women also had higher adjusted rates for antidepressant prescriptions before, during, and after the pregnancy. CONCLUSIONS: A significant psychiatric burden exists for women giving birth to children with FASD. Clinicians should recognise the high rates of psychiatric concerns facing mothers who give birth to children with FASD and should offer treatment and support to these women to improve their health and well-being and prevent further alcohol-exposed pregnancies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".