Toward Effective Identification of FASD
Bibliographic record
Abstract
The need to improve identification and understanding of individuals who have fetal alcohol spectrum disorder (FASD), including their strengths and challenges, is being increasingly recognized. Identification of FASD via screening is an important system-level intervention that may serve to improve early and accurate recognition of individuals who may have FASD, facilitate the provision of appropriately tailored support and interventions, and in doing so, foster healthy and positive outcomes for individuals and families. Effective and ethical implementation of FASD screening practices requires consideration of several factors for success, ensuring that resulting benefits outweigh potential harms. Using an implementation science framework, this topical review aims to provide an overview of these key considerations in order to guide further research and support practice and decision-making for service providers, organizations, and policy makers in the implementation of FASD identification and screening practices. These include prioritizing partnerships with stakeholders; taking a person-centered and ethical approach to FASD identification and screening; applying rigorous methodological research approaches to screening tool development, validation, and evaluation; increasing broader FASD awareness and response capacity at the system level; and advocating for continued policy reform and resources to enhance effective community-based support andinterventions following identification.
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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 | 0.038 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".