The past, present, and future of fetal alcohol spectrum disorder work in Newfoundland and Labrador: A landscape paper for change
Bibliographic record
Abstract
OBJECTIVES: In this paper, we provide an overview of best practices in FASD prevention, diagnostic, and interventions and supports. In Canada, people diagnosed with Fetal Alcohol Spectrum Disorder (FASD) represent a fraction people living with FASD. While social stigma may deter people from seeking an FASD diagnosis, other deterrents include the lack of screening and diagnostic referrals, cost of travelling to a clinic, and lack of clarity of how a diagnosis may improve supports and services. Preventing FASD and improving lifelong outcomes for people living with FASD requires a coordinated approach between prevention, diagnostic, intervention, and support efforts. METHODS: Using the example of Newfoundland and Labrador, a province where 60% of the population lives in rural communities and benefits from being involved in national initiatives and partnerships, we discuss efforts underway in other Canadian provinces to address FASD. RESULTS: We make three recommendations that begin to address FASD-specific needs in both rural and urban regions: a) a provincial FASD consultant position, b) an explicit partnership between provincial government and fasdNL, and c) increased access to FASD diagnostic teams. CONCLUSION: While the recommendations are both modest and essential first steps, we also suggest that collaborations and resource-sharing in FASD prevention and supports are more about doing things differently, rather than doing more.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".