Fetal Alcohol Spectrum Disorder: What does Public Awareness Tell Us about Prevention Programming?
Bibliographic record
Abstract
The prevalence of Fetal Alcohol Spectrum Disorder (FASD) does not appear to be diminishing over time. Indeed, recent data suggests that the disorder may be more prevalent than previously thought. A variety of public education programs developed over the last 20 years have promoted alcohol abstention during pregnancy, yet FASD remains a serious public health concern. This paper reports on a secondary data analysis of public awareness in one Canadian province looking at possible creative pathways to consider for future prevention efforts. The data indicates that the focus on women of childbearing age continues to make sense. The data also suggests that targeting formal (health care providers for examples) and informal support (partner, spouse, family, and friends) might also be valuable. They are seen as sources of encouragement, so ensuring they understand the risks, as well as effective ways to encourage abstinence or harm reduction, may be beneficial for both the woman and the pregnancy. Educating people who might support a woman in pregnancy may be as important as programs targeted towards women who may become or are pregnant. The data also suggests that there is already a significant level of awareness of FASD, thus highlighting the need to explore the effectiveness and value of current prevention approaches.
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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 | 0.009 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".