The Potential for Fetal Alcohol Spectrum Disorder Prevention of a Harmonized Approach to Data Collection about Alcohol Use in Pregnancy Cohort Studies
Bibliographic record
Abstract
Prenatal alcohol exposure is a leading cause of disability, and a major public health concern in Canada. There are well-documented barriers for women and for service providers related to asking about alcohol use in pregnancy. Confidential research is important for learning about alcohol use before, during and after pregnancy, in order to inform fetal alcohol spectrum disorder (FASD) prevention strategies. The Research Advancement through Cohort Cataloguing and Harmonization (ReACH) initiative provides a unique opportunity to leverage the integration of the Canadian pregnancy and birth cohort information regarding women's drinking during pregnancy. In this paper, we identify: The data that can be collected using formal validated alcohol screening tools; the data currently collected through Canadian provincial/territorial perinatal surveillance efforts; and the data currently collected in the research context from 12 pregnancy cohorts in the ReACH Catalogue. We use these findings to make recommendations for data collection about women's alcohol use by future pregnancy cohorts, related to the frequency and quantity of alcohol consumed, the number of drinks consumed on an occasion, any alcohol consumption before pregnancy, changes in use since pregnancy recognition, and the quit date. Leveraging the development of a Canadian standard to measure alcohol consumption is essential to facilitate harmonization and co-analysis of data across cohorts, to obtain more accurate data on women's alcohol use and also to inform FASD prevention strategies.
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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 | 0.612 | 0.688 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".