Fetal alcohol spectrum disorder resources for health professionals: a scoping review
Bibliographic record
Abstract
OBJECTIVES: This scoping review aimed to identify and critically appraise resources for health professionals to identify, diagnose, refer, and support individuals with fetal alcohol spectrum disorder (FASD)-including the extent to which the resources are appropriate for use in communities with First Nations Peoples. METHOD: Seven peer-reviewed databases (April 2022) and 14 grey literature websites (August 2022) were searched. The reference lists of all sources that underwent full-text review were handsearched, and FASD experts were consulted for additional sources. Resources were assessed using the Appraisal of Guidelines for REsearch and Evaluation II instrument and an adapted version of the National Health and Medical Research Council FORM Framework and iCAHE Guideline Quality Checklist. RESULTS: A total of 41 resources underwent data extraction and critical appraisal, as screening and/or diagnosis guidelines were excluded because they are covered in other reviews. Most were recently published or updated (n=24), developed in the USA (n=15, 36.6%) or Australia (n=12, 29.3%) and assisted with FASD patient referral or support (n=40). Most management guidelines scored 76%-100% on overall quality assessment (n=5/9) and were recommended for use in the Australian context with modifications (n=7/9). Most of the guides (n=15/22) and factsheets (n=7/10) received a 'good' overall score. Few (n=3/41) resources were explicitly designed for or with input from First Nations Australians. CONCLUSION: High-quality resources are available to support health professionals providing referrals and support to individuals with FASD, including language guides. Resources should be codesigned with people living with FASD to capture and integrate their knowledge and preferences.
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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 | 0.044 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.033 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".