Fetal Alcohol Spectrum Disorder (FASD) and Competency to Stand Trial (CST): A Call on Forensic Evaluators to Become Informed
Bibliographic record
Abstract
Fetal Alcohol Spectrum Disorder affects an estimated 3–5% of the population and results in numerous functional deficits in cognitive, social, and adaptive skills. Additionally, it is estimated that 60% of those who have FASD will become involved in the criminal justice system at some point in their life. Given the high percentage of those with FASD who become involved in the criminal justice system and the significant functional deficits that they experience, it is essential that forensic evaluators become familiar with FASD. It is also important that evaluators are familiar with how this disorder may impair an individual’s ability to participate adequately in the criminal justice system, including competency to stand trial. This article will provide some basic information about the need for forensic evaluators, and those who may refer defendants to forensic evaluators (e.g., attorneys, social workers, jail mental health staff), to become informed about FASD in order to conduct thorough and complete evaluations of competence to stand trial. Because little research has been completed on this issue, information presented in this article is based on literature review and clinical experiences. Basic information about the symptoms of FASD that impact competence to stand trial (CST) as well as proposed techniques for conducting a thorough assessment of CST when relevant FASD symptoms are present will be discussed. Finally, recommendations for training forensic mental health professionals about FASD and its potential impact on a defendant’s competency-related abilities are offered and suggestions for future research are presented.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".