Prevalence and patterns of alcohol use in pregnancy in remote <scp>W</scp>estern <scp>A</scp>ustralian communities: The <scp>L</scp>ililwan<scp>P</scp>roject
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Alcohol use in pregnancy is thought to be common in remote Australian communities, but no population-based data are available. Aboriginal leaders in remote Western Australia invited researchers to determine the prevalence and patterns of alcohol use in pregnancy within their communities. DESIGN AND METHODS: A population-based survey of caregivers of all children born in 2002/2003 and living in the Fitzroy Valley in 2010/2011 (n = 134). Alcohol use risk was categorised using the Alcohol Use Disorders Identification Test consumption subset (AUDIT-C) tool. Birth and child outcomes were determined by interview, medical record review and physical examination. RESULTS: 127/134 (95%) eligible caregivers participated: 78% were birth mothers, 95% were Aboriginal and 55% reported alcohol use in index pregnancies; 88% reported first trimester drinking and 53% drinking in all trimesters. AUDIT-C scores were calculated for 115/127 women, of whom 60 (52%) reported alcohol use in pregnancy. Of the 60 women who drank (AUDIT-C score ≥ 1), 12% drank daily/almost daily, 33% drank 2-3 times per week; 71% drank ≥ 10 standard drinks on a typical occasion; 95% drank at risky or high-risk levels (AUDIT-C score ≥ 4). Mean AUDIT-C score was 8.5 ± 2.3 (range 2-12). The most common drinking pattern was consumption of ≥ 10 standard drinks either 2-4 times per month (27%) or 2-3 times per week (27%). DISCUSSION AND CONCLUSIONS: High-risk alcohol use in pregnancy is common in remote, predominantly Aboriginal communities in north western Australia. Prevention strategies to reduce prenatal alcohol use 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".