Interventions for Fetal Alcohol Spectrum Disorder: Meeting Needs Across the Lifespan
Bibliographic record
Abstract
Background: Fetal Alcohol Spectrum Disorder (FASD) is a complicated disability resulting in a diverse, highneeds population for whom intervention is critical to optimize functional competence and reduce the emergence of adverse outcomes.Researchers have been evaluating intervention efforts for FASD to inform practice and policy decision-making. Objective:The current review provides a synopsis of the current state of evidence for intervention research in FASD, with consideration of how our growing understanding of the unique needs of individuals with FASD might inform future intervention initiatives.Method: A comprehensive literature review was conducted across a number of databases using multiple search terms linking FASD and intervention.Results: Existing evidence-based interventions are limited and focus predominantly on the school-aged population, thereby neglecting adolescents and adults.Future research efforts are needed to support individuals with FASD across the entire lifespan, particularly during transitions and for individuals involved in the legal system.Conclusion: As the field of FASD continues to grow, so must the quality and quantity of intervention research.It is through cross-discipline intervention research efforts that the evidence supporting best practices will be established, and policies can be implemented to reflect best uses of available funds to support this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".