Fetal Alcohol Spectrum Disorder and Neurodevelopmental Disorders: An International Practice Survey of Forensic Mental Health Clinicians
Bibliographic record
Abstract
Individuals with fetal alcohol spectrum disorder (FASD), a neurodevelopmental disorder caused by prenatal exposure to alcohol, are overrepresented in criminal justice settings and have complex, forensically relevant clinical needs. This study surveyed 81 forensic clinicians recruited via international professional association listserv postings and social media about their assessment and intervention practices in providing services to clients with FASD and other neurodevelopmental disorders (NDDs), along with their training experiences and needs in this area. Results indicated that the majority of clinicians had forensic experience working with clients who had FASD and other NDDs, although most identified limited relevant training experiences, gaps in their readiness for service provision, and practice barriers in effectively working with these populations. Clinicians also reported seeing fewer clients with FASD relative to other NDDs, and feeling less prepared for forensic practice with this population. Most clinicians endorsed the need for additional training and supports to increase their competency and enhance their practice, such as the development of screening tools, clinical guidelines, and access to experts or specialists for consultation. As awareness about FASD continues to grow in legal contexts, additional research, training, and policy consideration is required to develop and implement evidence-based practice resources for forensic clinicians.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".