A global review of the impact on women from men’s alcohol drinking: the need for responding with a gendered lens
Bibliographic record
Abstract
BACKGROUND: Global evidence shows that men's harmful alcohol use contributes to intimate partner violence (IPV) and other harms. Yet, interventions that target alcohol-related harms to women are scarce. Quantitative analyses demonstrate links with physical and verbal aggression; however, the specific harms to women from men's drinking have not been well articulated, particularly from an international perspective. AIM: To document the breadth and nature of harms and impact of men's drinking on women. METHODS: A narrative review, using inductive analysis, was conducted of peer-reviewed qualitative studies that: (a) focused on alcohol (men's drinking), (b) featured women as primary victims, (c) encompassed direct/indirect harms, and (d) explicitly featured alcohol in the qualitative results. Papers were selected following a non-time-limited systematic search of key scholarly databases. RESULTS: Thirty papers were included in this review. The majority of studies were conducted in low- to middle-income countries. The harms in the studies were collated and organised under three main themes: (i) harmful alcohol-related actions by men (e.g. violence, sexual coercion, economic abuse), (ii) impact on women (e.g. physical and mental health harm, relationship functioning, social harm), and (iii) how partner alcohol use was framed by women in the studies. CONCLUSION: Men's drinking results in a multitude of direct, indirect and hidden harms to women that are cumulative, intersecting and entrench women's disempowerment. An explicit gendered lens is needed in prevention efforts to target men's drinking and the impact on women, to improve health and social outcomes for women worldwide.
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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 | 0.024 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".