The sexual behaviour and sexual health of heterosexual-identifying men who have sex with men: understanding an understudied population to inform public health policy and practice
Bibliographic record
Abstract
Introduction: Sexual behaviour and identity do not always align. A small but significant proportion of men who have sex with men identify as heterosexual (H-MSM). H-MSM are a challenging population to research, and while their sexual health needs may be complex, these are currently poorly understood. This has important potential consequences for these men, their sexual partners, and the wider population. In this thesis, I use complementary methods to investigate the sexual behaviour and sexual health of H-MSM in high-income countries and consider the implications for public health policy and practice. \nMethods: I systematically reviewed 43 quantitative and 21 qualitative studies of H-MSM published 2008-2018 to summarise previous research. I harmonised data for 196,426 MSM from cross-sectional surveys conducted 2010-2017 in Australia, Canada, New Zealand and 14 Western European countries, and performed individual participant data meta-analysis to produce robust statistical estimates and associations giving insight into sexual behaviours and sexual health outcomes related to STI/HIV transmission and testing. I conducted 15 semi-structured interviews with H-MSM resident in England, to explore H-MSM’s decision-making regarding STI/HIV prevention and accessing sexual healthcare. \nResults: H-MSM were less likely than gay-identifying MSM, and in some cases bisexual-identifying MSM, to report sexual behaviours associated with STI/HIV transmission, or to test for STI/HIV - even among H-MSM reporting greater testing need. Qualitative evidence suggested that H-MSM considered their own health and that of their female partners when making STI/HIV prevention and testing decisions, while lack of information and privacy concerns limited their access to sexual healthcare and STI/HIV prevention. \nConclusions: H-MSM are vulnerable to poor sexual health and face specific challenges in accessing sexual healthcare. My work informs novel approaches that might improve engagement between this population and health services to provide effective and holistic healthcare that generates both individual and public health benefits.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".