Children’s experience of physical harms and exposure to family violence from others’ drinking in nine societies
Bibliographic record
Abstract
AIM: To study caregiver reports of children's experience of physical harm and exposure to family violence due to others' drinking in nine societies, assess the relationship of harm with household drinking pattern and evaluate whether gender and education of caregiver affect these relationships. METHOD: Using data on adult caregivers from the GENAHTO (Gender and Alcohol's Harm to Others) project, child alcohol-related injuries and exposure of children to alcohol-related violence (CAIV) rates are estimated by country and pooled using meta-analysis and stratified by gender of the caregiver. Households with and without heavy or harmful drinker(s) (HHD) are compared assessing the interaction of caregiver gender on the relationship between reporting HHD and CAIV, adjusting for caregiver education and age. Additionally, the relationship between caregiver education and CAIV is analysed with meta-regression. RESULTS: The prevalence of CAIV varied across societies, with an overall pooled mean of 4% reported by caregivers. HHD was a consistent correlate of CAIV in all countries. Men and women in the sample reported similar levels of CAIV overall, but the relationship between HHD and CAIV was greater for women than for men, especially if the HHD was the most harmful drinker. Education was not significantly associated with CAIV. CONCLUSION: One in 25 caregivers with children report physical or family violence harms to children because of others' drinking. The adjusted odds of harm are significantly greater (more than four-fold) in households with a heavy or harmful drinker, with men most likely to be defined as this drinker in the household.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".