Fetal Alcohol Spectrum Disorders: Survey of Healthcare Providers after Continuing Education
Bibliographic record
Abstract
Fetal alcohol spectrum disorders (FASD) occur as a result of prenatal alcohol exposure and are commonly associated with intellectual disability. Maternal alcohol consumption affects fetal development resulting in numerous lifelong physical, mental, and neurobehavioral abnormalities. To promote prevention of prenatal alcohol exposure and intervention to mitigate alcohol’s postnatal effects, the Centers for Disease Control and Prevention (CDC) provides continuing education to healthcare providers through their FASD Regional Training Centers (RTCs). An online survey evaluated healthcare providers’ perceived competency after training. Cover letters with the survey link were electronically mailed to healthcare providers, who received training between 2002 and 2009 from the Midwest and Southeast RTCs. Eighty-two providers who treated women or children responded to the survey (7.5% response rate). Approximately 86% of providers who treated women have identified women ‘at risk’ for alcohol abuse with 90% indicating they would refer to Substance Abuse or Mental Health Services. However, over 25% perceived lack of training and limited time as barriers in treating women of childbearing age for at-risk drinking. Over 90% of providers who treated children reported feeling competent in recognizing FAS and other alcohol-related effects. Yet, only 23% of providers for children reported using FASD diagnostic schema and were more apt to use growth charts (70%) rather than lip philtrum guides (58%) or palpebral fissure length measurements (50%), tools typically used in FAS determination. These results suggest a need for training to focus on methodology that assists providers to easily incorporate screening, diagnostic, and treatment procedures into their daily practice
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".