Exploring Canadian Public Perceptions of Fetal Alcohol Spectrum Disorder
Bibliographic record
Abstract
With a national prevalence of 4%, fetal alcohol spectrum disorder (FASD) is a leading developmental disability in Canada. FASD has lifelong effects on cognitive, emotional, behavioral, social, and physical health. Despite its significant prevalence, FASD is poorly understood and highly stigmatized, which can exacerbate challenges and limit access to supports. This study explored knowledge of FASD among 372 Canadian adults to identify common knowledge gaps and misconceptions. Participants were recruited through the Angus Reid Forum for a larger study on FASD and other neurodevelopmental disorders. In the current study, we analyzed responses to one open-ended question which asked participants to report what they know about FASD. Using qualitative content analysis, five categories emerged from the data: (1) general knowledge, (2) causes, (3) perceived impacts, (4) nature of FASD and (5) stigmatizing attitudes. The results suggest a wide range of knowledge and awareness. While some participants had a strong understanding, some were not aware of FASD, and others believed misconceptions and held stigmatizing beliefs. Participants who had direct experiences with individuals with FASD had a more accurate understanding. The findings suggest the need for targeted education campaigns to address misconceptions, reduce stigma, and promote a more informed understanding of FASD.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".