Prevention of Fetal Alcohol Spectrum Disorder: Current Canadian Efforts and Analysis of Gaps
Bibliographic record
Abstract
Effective prevention of risky alcohol use in pregnancy involves much more than providing information about the risk of potential birth defects and developmental disabilities in children. To categorize the breadth of possible initiatives, Canadian experts have identified a four-part framework for fetal alcohol spectrum disorder (FASD) prevention: Level 1, public awareness and broad health promotion; Level 2, conversations about alcohol with women of childbearing age and their partners; Level 3, specialized support for pregnant women; and Level 4, postpartum support for new mothers. In order to describe the level of services across Canada, 50 Canadian service providers, civil servants, and researchers working in the area of FASD prevention were involved in an online Delphi survey process to create a snapshot of current FASD prevention efforts, identify gaps, and provide ideas on how to close these gaps to improve FASD prevention. Promising Canadian practices and key areas for future action are described. Overall, Canadian FASD prevention programming reflects evidence-based practices; however, there are many opportunities to improve scope and availability of these initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".