Ethical challenges in FASD prevention: Scientific uncertainty, stigma, and respect for women’s autonomy
Bibliographic record
Abstract
Fetal alcohol spectrum disorder (FASD) is a leading form of neurodevelopmental delay in Canada, affecting an estimated 3000 babies per year. FASD involves a range of disabilities that entail significant costs to affected individuals, families, and society. Exposure to alcohol in utero is a necessary factor for FASD development, and this has led to FASD being described as "completely preventable". However, there are significant ethical challenges associated with FASD prevention. These challenges revolve around 1) what should be communicated about the risks of alcohol consumption during pregnancy, given some ongoing scientific uncertainty about the effects of prenatal alcohol exposure, and 2) how to communicate these risks, given the potential for stigma against women who give birth to children with FASD as well as against children and adults with FASD. In this paper, we share initial thoughts on how primary care physicians can tackle this complex challenge. First, we recommend honest disclosure of scientific evidence to women and the tailoring of information offered to pregnant women. Second, we propose a contextualized, patient-centred, compassionate approach to ensure that appropriate advice is given to patients in a supportive, non-stigmatizing way.
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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 | 0.111 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.064 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.021 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 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".