Prevalence of fetal alcohol spectrum disorder in foster care: A scoping review
Bibliographic record
Abstract
The prevalence of fetal alcohol spectrum disorder (FASD) has been reported to be disproportionately high among children in foster care compared with the general population. However, updated prevalence estimates of infants and children with FASD in foster care or the prevalence of placement of children with FASD in foster care make this unclear. This study examines two questions. Firstly, what is the prevalence of FASD among infants and children in foster care? Secondly, what is the likelihood of placement in foster care for infants and children with FASD? This review was designed using PRISMA-SCR and JBI scoping review guidelines. Three databases were searched for the period June 2012 to June 2023: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Google Scholar for all countries. Overall prevalence estimates were calculated using a complementary log-log link model along with 95% confidence intervals. Firstly, the estimated prevalence of FASD among infants and children in foster care was 18.8%. Secondly, among children diagnosed with FASD 30.5% are placed into foster care, reflecting greatly increased risk of placement of infants and children with FASD in foster care. We conclude that routine screening for FASD is needed to improve the identification of infants and children with FASD. Increased attention is also needed on developing strategies for FASD prevention. Recognition that nearly one of every three children with FASD will enter foster care demonstrates the need for increased funding, enhanced training and greater availability of services for families and children impacted by FASD.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".