Costs of fetal alcohol spectrum disorder in Alberta, Canada.
Bibliographic record
Abstract
BACKGROUND: Although many programs targeting fetal alcohol spectrum disorder (FASD) are implemented, the province of Alberta is still lacking information on costs of FASD. OBJECTIVES: To estimate the costs of FASD in Alberta based on available US and Canadian research on costs of FASD, and Alberta data. METHODS: Two types of costs were estimated. The annual long-term economic cost of FASD, which referred to a projected amount of money incurred by lives of the cohort of children born with FASD each year, was estimated by multiplying the lifetime cost of caring for each child born with FASD with the number of children born with FASD each year. The annual short-term economic cost of FASD, which referred to the amount of money incurred by people who are presently living with FASD, was estimated by using a FASD cost calculator online at http://www.online-clinic.com. Both were societal costs adjusted to 2008 Canadian dollars. RESULTS: The annual long-term economic cost from the disorders rose from $130 to $400 million each year for the Alberta economy. The annual short-term economic cost for FASD in Alberta was from $48 to $143 million, and the daily cost for FASD in Alberta was from $105 to $316 thousand. CONCLUSION: These numbers suggest a need for a provincial FASD prevention strategy. The costs of FASD can be used to evaluate the benefits of prevention programs to society.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".