Article Commentary: Becoming FASD Informed: Strengthening Practice and Programs Working with Women with FASD
Bibliographic record
Abstract
There is growing appreciation among health and social care providers, especially those working in community-based programs with women or young people with substance use problems and/or who have experienced violence, maltreatment, or trauma, that a high number of their program participants may have been prenatally exposed to alcohol or have fetal alcohol spectrum disorder (FASD). This article provides a conceptualization of the key components of an FASD-informed approach. Drawing on the emerging literature and the author's research identifying the support needs and promising approaches in working with women, young adults, and adults with FASD, as well as evaluations of FASD-related programs, the article discusses what an FASD-informed approach is, why it is centrally important in working with women, adults, and young people who may have FASD, underlying principles of an FASD-informed approach, and examples of FASD-informed adaptations to practice, programming, and the physical environment. In this discussion, the benefits of using an FASD-informed approach for service providers and women living with FASD and their families, as well as conceptualization of FASD-informed policy and systems are highlighted.
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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.058 | 0.045 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".