Harms to children from men’s heavy drinking: A scoping review
Bibliographic record
Abstract
Background: Research has examined a range of impacts of women's drinking on children. However, fewer studies have addressed the consequences of men's heavy drinking on children. This review aims to identify and describe men's behaviours and actions that arise from their drinking and are likely to cause harm to children, and the types of harms that stem from men's drinking that children experience. Methods: Review articles from 1990 to September 2023 were included. Four health and social databases were searched for reviews that included terms relating to men, alcohol consumption, and child harms. From 1,873 identified articles, 19 articles met the criteria for inclusion. Results: Four broad topics were identified: relationships and families; violence and maltreatment; health, psychological and wellbeing impacts; and social and educational outcomes for children. Children may experience both direct and indirect harms associated with men's drinking. The former includes examples where men's drinking was linked to disinhibition, aggression, and physical assault. In the latter, men were absent resulting in being emotionally unavailable due to drinking. Internalising and externalising behaviours were common outcomes for children exposed to men's drinking. Contextual factors such as poverty, cultural norms and patriarchal influences were interrelated and sometimes exacerbated harm to children from men's drinking. Conclusions: This review highlights the immediate and longer-lasting consequences of men's drinking for children, including family conflict, maltreatment, and emotional distress. Future studies should address gender disparities and consider the social factors present in cases where children are exposed to men's drinking.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".