Bibliographic record
Abstract
Elliott, E. (2014). Australia plays ‘catch-up’ with Fetal Alcohol Spectrum Disorders. The International Journal Of Alcohol And Drug Research, 3(1), 121-125. doi:http://dx.doi.org/10.7895/ijadr.v3i1.177Australians are amongst the highest consumers of alcohol worldwide, and "risky" drinking is increasing in young women. Contrary to the advice in national guidelines, drinking in pregnancy is common. Many women don’t understand the potential for harm to the unborn child and 20% have a "tolerant" attitude to drinking during pregnancy. As attitude, rather than knowledge, predicts risk of drinking in a future pregnancy, this presents a challenge for public health campaigns. Alcohol is teratogenic, crosses the placenta, and contributes to a range of physical, developmental, learning and behavioural problems, including fetal alcohol spectrum disorders (FASD). As nearly half of all pregnancies in Australia are unplanned, inadvertent exposure to alcohol is common. Good-quality prevalence data on FASD are lacking in Australia, although alcohol use at "risky" levels is well documented in some disadvantaged communities. In the last decade, clinicians, researchers, governments and non-governmental organizations have shown renewed interest in addressing alcohol use in pregnancy and FASD. This has included a parliamentary inquiry into FASD, provision of targeted funding for FASD, and development of educational materials for health professionals and the general public. Key challenges for the future are to prevent FASD and to offer timely diagnosis and help to children and families living with FASD. The implementation of evidence-based interventions known to decrease access to, and excessive use of, alcohol in our society will aid in the prevention of FASD. The development of national diagnostic tools for screening and diagnosis, and the training of health professionals in the management of FASD, are urgently needed.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".