Fetal Alcohol Spectrum Disorder (FASD) in New Brunswick’s Justice System: Exploring the Dynamic Interplay of knowledge, Attitudes, and Practices (KAP) Among Justice Professionals
Bibliographic record
Abstract
Fetal Alcohol Spectrum Disorder (FASD) is a diagnostic term describing the lifelong impacts of prenatal alcohol exposure (PAE). As one of Canada’s most prevalent neurodevelopmental disabilities, FASD conservatively affects approximately 4% of the population. PAE disrupts development, increasing susceptibility to adverse childhood events (ACEs) and poor life outcomes such as mental health challenges and disrupted schooling experiences, which, when left unsupported, can contribute to pathways to criminal legal involvement. Despite the overrepresentation of individuals with FASD in criminal legal settings, gaps persist between knowledge and practice. In New Brunswick (NB), where FASD-related funding, research, and resources are limited, individuals with FASD face amplified challenges. Addressing regional disparities is crucial to mitigate inequities for justice-involved individuals with FASD. The current study investigates gaps in understanding and supports for justice-involved individuals with FASD. The study addresses the following question: What do justice professionals in NB know, think, and do about FASD? Data collection included a 10–12-minute online Qualtrics survey and 30–45-minute online interviews. Surveys explored justice professionals’ knowledge, attitudes, practices, and knowledge-sharing preferences, while interviews provided nuanced insights and in-depth contextualization. Survey respondents and interviewees revealed discrepancies in FASD-related knowledge, attitudes, and practices. Findings will inform the NB FASD Centre of Excellence’s plans to advocate for and advance FASD-informed policies in NB, such as mandatory training for justice professionals. Collaborating with interest-holders ensures policy relevance and meaningful support for individuals with FASD, fostering an equitable and inclusive justice system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".