Mental Health and Affect Regulation Impairment in Fetal Alcohol Spectrum Disorder (FASD): Results from the Canadian National FASD Database
Bibliographic record
Abstract
AIMS: Individuals with fetal alcohol spectrum disorder (FASD) frequently have challenges with regulating emotional arousal, or affect regulation (AR), and experience high rates of mental health disorders. This study examined children and adults with FASD to investigate the relationship between AR impairment and several mental health problems and diagnoses. METHODS: Data from the Canadian national FASD database was used for analysis. Seven mental health diagnoses, including attention-deficit/hyperactivity disorder, post-traumatic stress disorder, conduct disorder, attachment disorder, intellectual disability, and language disorder were examined. A history of suicidality was also examined. The prevalence of these mental health problems in individuals with and without AR impairment was compared. RESULTS: Individuals with FASD and AR impairment were significantly more likely to be diagnosed with conduct disorder (OR 4.8), attachment disorder (OR 6.1), or post-traumatic stress disorder (OR 8.1) when compared to those without AR impairment. They were also more likely to have a history of suicidality (OR 8.6). AR impairment was most commonly found in those with greater overall neurodevelopmental impairment. Having AR impairment was associated with receiving a diagnosis of FASD at a later age, but was not related to gender, intellectual disability, or language disorder. CONCLUSION: AR impairment is strongly related to several mental health diagnoses in those with FASD and presents some promising possibilities for targeted early intervention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".