Intimate Partner Violence and Social Pressure among Gay Men in Six Countries
Bibliographic record
Abstract
INTRODUCTION: Recent research suggests that men who have sex with men (MSM) experience intimate partner violence (IPV) at significantly higher rates than heterosexual men. Few studies, however, have investigated implications of heterosexist social pressures - namely, homophobic discrimination, internalized homophobia, and heterosexism - on risk for IPV among MSM, and no previous studies have examined cross-national variations in the relationship between IPV and social pressure. This paper examines reporting of IPV and associations with social pressure among a sample of internet-recruited MSM in the United States (U.S.), Canada, Australia, the United Kingdom, South Africa, and Brazil. METHODS: We recruited internet-using MSM from 6 countries through selective banner advertisements placed on Facebook. Eligibility criteria were men age over 18 reporting sex with a man in the past year. Of the 2,771 eligible respondents, 2,368 had complete data and were included in the analysis. Three outcomes were examined: reporting recent experience of physical violence, sexual violence, and recent perpetration of physical violence. The analysis focused on associations between reporting of IPV and experiences of homophobic discrimination, internalized homophobia, and heteronormativity. RESULTS: Reporting of experiencing physical IPV ranged from 5.75% in the U.S. to 11.75% in South Africa, while experiencing sexual violence was less commonly reported and ranged from 2.54% in Australia to 4.52% in the U.S. Perpetration of physical violence ranged from 2.47% in the U.S. to 5.76% in South Africa. Experiences of homophobic discrimination, internalized homophobia, and heteronormativity were found to increase odds of reporting IPV in all countries. CONCLUSION: There has been little data on IPV among MSM, particularly MSM living in low- and middle-income countries. Despite the lack of consensus in demographic correlates of violence reporting, heterosexist social pressures were found to significantly increase odds of reporting IPV in all countries. These findings show the universality of violence reporting among MSM across countries, and highlight the unique role of heteronormativity as a risk factor for violence reporting among MSM. The results demonstrate that using internet-based surveys to reach MSM is feasible for certain areas, although modified efforts may be required to reach diverse samples of MSM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".