Hearing the voice of child welfare social workers: planning safe care for a child with or suspected of having fetal alcohol spectrum disorders (FASDs)
Bibliographic record
Abstract
Purpose In the early 1970s, clinical evidence emerged documenting causal links between prenatal alcohol exposure (PAE) and children’s behaviors as observed by child welfare social workers (CWSWs). Unfortunately, fetal alcohol spectrum disorders (FASD) remain on the margins of public health priorities. The purpose of this study was to elicit the views of child welfare social workers when responding to case of or suspected FASD. Design/methodology/approach A sample (N = 18) of CWSWs, allied health professionals and foster parents were interviewed. Findings Findings indicate that social workers struggle with their statutory duty to plan safe care for children with or suspected of having FASD. Emergent themes include struggling with advocacy, professional devaluation and lack of procedural guidance. Practical implications Social workers need a clear pathway and FASD knowledge to guide their interventions and enhance their capacity to advocate for affected children. Originality/value An abundance of research documents the direct effect of PAE on physical, cognitive and behavioral outcomes. However, few studies focus on the critical interface of children with an FASD entering public care and the social workers responsible for planning their safe care. This study sought to document social workers’ response to this vulnerable cohort of children.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".