A Social Work student’s developing understanding of Fetal Alcohol Spectrum disorder from medical
Bibliographic record
Abstract
Research on FASD has focused on medical/scientific findings (Gallicano, 2010; Jones, & Smith, 1973; Streissguth, Bookstein, Barr, Sampson, O’Malley, & Young, 2004). Continued medical research is thought to be helpful to our understanding of the origins of FASD and the interventions that help to address the psychosocial needs of persons with FASD. However, this paper stresses the need to also focus on the social aspects of FASD and the implications for human service workers who provide support for persons with FASD and their families. Gough and Fuchs (2006) indicate that although there is a growing prevalence of FASD-related disabilities, there is not a substantive knowledge base on child welfare practice in response to FASD. The need for human service workers to look beyond identifying a condition and addressing symptoms is emphasized. The author suggests human service workers need to prepare to work with children with FASD by reflectively appreciating the economic, legal, and cultural influences affecting persons as well as medically- and evidenced-based interventions. Keywords: Fetal Alcohol Syndrome Disorder (FASD), medical perspectives, intervention, social support, human service workers.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".