Alcohol, Smoking, and Drug Use Among Inuit Women of Childbearing Age During Pregnancy and the Risk to Children
Bibliographic record
Abstract
BACKGROUND: Alcohol consumption during pregnancy, a known teratogen often associated with drug use and smoking is a well-known public health concern. AIM: This study provides prevalence data for alcohol, smoking, and illicit drug use before, during, and after pregnancy among Inuit. Factors associated with alcohol use are also identified. METHODS: Two hundred and eight Inuit women from Arctic Quebec were interviewed at mid-pregnancy, and at 1 and 11 months postpartum to provide descriptive data on smoking, alcohol, and drug use during pregnancy, and the year before and after pregnancy. Sociodemographic and family characteristics potentially associated with alcohol use were documented. RESULTS: Ninety-two percent of the women reported smoking and 61% reported drinking during pregnancy. Episodes of binging during pregnancy were reported by 62% of the alcohol users, which correspond to 38% of pregnant women. Thirty-six percent of the participants reported using marijuana during pregnancy. Alcohol use and binge drinking during pregnancy were more likely to be reported by women who lived in less crowded houses, had a better knowledge of a second language, drank alcohol more often and in larger amounts prior to pregnancy, and used illicit drugs. Binge drinkers were more likely to be single women and to have had fewer previous pregnancies. Postpartum distress and violence were more likely to be experienced by women who used alcohol during pregnancy. Binge drinking during pregnancy was best predicted by drinking habits before pregnancy, maternal symptoms of depression, the use of illicit drugs during pregnancy, and the number of young children living with the mother. CONCLUSIONS: These results confirm that alcohol is a major risk factor to maternal and child health in this population, underscoring the need for culturally relevant and effective prevention programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".