Mental health issues in fetal alcohol spectrum disorder
Bibliographic record
Abstract
BACKGROUND: High numbers of individuals with Fetal Alcohol Spectrum Disorders (FASD) have been described as having mental health problems. AIMS: This article summarizes research about mental health problems in FASD and considers related developmental and environmental issues. METHOD: A computer-based literature search was conducted in the databases Medline, PsycINFO, Google Scholar, Academic Search Complete, and Education Resources Information Centre for articles addressing the prevalence and types of mental health issues in individuals affected by FASD. RESULTS: High rates of mental disorders within the FASD and prenatal alcohol exposure (PAE) population were found to be consistently reported for both internalizing and externalizing disorders. Moreover, problems that emerge in childhood may reflect a convergence of genetic, environmental, and neurophysiological factors that persist into adulthood. CONCLUSIONS: Researchers are beginning to document the impacts of PAE on later mental health development. Further longitudinal study is needed to determine whether there is an increasing severity of mental health deficits and consequences with age, and whether any such changes reflect increasingly deteriorating environmental factors or brain-based factors. Additionally, research is needed to design interventions to better address the unique mental health needs of this population.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".