Social demography of alcohol‐related harm to children in Australia
Bibliographic record
Abstract
AIMS: This study seeks to establish the prevalence alcohol-related harms to children (ARHC) that occur because of others' drinking in the general population and examine how this varies by who was reported to have harmed the child and socio-demographic factors. DESIGN AND SETTING: A randomly selected cross-sectional national population telephone survey undertaken in 2008 in Australia. PARTICIPANTS: A total of 1142 adult respondents who indicated they lived with or had a parental/carer role for children. MEASUREMENTS: Questions included whether children had been negatively affected in any way, left unsupervised or in an unsafe situation, verbally abused, physically hurt or exposed to serious family violence because of others' drinking in the past year. FINDINGS: Twenty-two per cent of respondents reported children had been affected because of another's drinking in the past year; 3% reported substantial harm. Respondents most commonly reported that children were verbally abused because of others' drinking (9%). Participants in single-carer households were more likely to report ARHC than participants in households with two carers, and participants who drank weekly were more likely to report ARHC than those who did not drink. CONCLUSIONS: Almost a quarter of those with a caring role for children in Australia reported that a child or children with whom they lived or for whom they were responsible have been affected adversely by others' alcohol consumption in the past year. The problem extends across the social spectrum, but children in single-parent homes may be at higher risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".