Drinking alcohol during pregnancy: evidence from Canadian Community Health Survey 2007/2008.
Bibliographic record
Abstract
BACKGROUND: Drinking alcohol during pregnancy may cause many health problems for the child, one of which is fetal alcohol spectrum disorder (FASD). Since FASD is incurable, actions meant to prevent the occurrence of the disability by targeting drinking women become more important. Epidemiological data on drinking among pregnant women, including prevalence and determinants/risk factors, is essential for designing and evaluating prevention programs. OBJECTIVES: To estimate the prevalence of drinking alcohol during pregnancy and examine the determinants of this behaviour. METHODS: Using the 2007/8 Canadian Community Health Survey (CCHS) data, we estimated the weighted prevalence of women who drank alcohol during their last pregnancy by provinces. We used a weighted logistic regression to examine associations between drinking patterns, substance abuse behaviours, health-related and socio-demographic characteristics of the women, and the outcome variable. RESULTS: There were two main findings of this study. One was that the 2007/8 prevalence of drinking alcohol during pregnancy in ON, BC, and Canada was estimated at 5.4%, 7.2%, and 5.8%, respectively. The other was that the use of general practitioners (GP) or family physicians (FP) associated with a decreased risk of drinking alcohol during pregnancy. DISCUSSION: The results suggest that interventions that involve GP or FP and that increase the use of GP or FP by pregnant women can be effective in reducing drinking alcohol during pregnancy.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".