Internet-based partner selection and risk for unprotected anal intercourse in sexual encounters among men who have sex with men: a meta-analysis of observational studies
Bibliographic record
Abstract
INTRODUCTION: Men who have sex with men (MSM) who identify sex partners over the internet are more likely than other MSM to report having unprotected anal intercourse (UAI). It is unclear whether the internet facilitates pursuit of high-risk sex or whether MSM seeking sex online are a higher-risk population than other MSM. To summarise evidence as to whether internet-based partner selection predisposes MSM to high-risk behaviour, we conducted a meta-analysis of observational studies comparing MSM's UAI risk in online-initiated encounters to their UAI risk in offline-initiated encounters. METHODS: We systematically searched published, peer-reviewed literature to identify studies reporting MSM participants' engagement in UAI with online-identified and offline-identified male partners. We calculated pooled odds ratios for any UAI and for seroadaptive UAI practices with partners identified online relative to partners identified offline. RESULTS: We included 11 studies representing 39,602 sexual encounters. Odds for any UAI, seroconcordant UAI and serodiscordant UAI with strategic positioning were higher in online-initiated than offline-initiated encounters. Odds for UAI in group sex were higher in online-initiated encounters only among HIV-positive MSM. Effect sizes for all outcomes were greater among HIV-positive than HIV-negative MSM. Effect sizes were greatest when bathhouses, saunas and sex resorts were treated as offline comparison venues. CONCLUSIONS: Encounters initiated online have elevated odds for entailing UAI and seroadaptive UAI practices. Online-delivered behavioural interventions should address insufficiency of risk-reducing practices involving UAI relative to consistent condom use and promote frequent HIV testing among MSM seeking UAI partners online. http://group.bmj.com/products/journals/instructions-for-authors/licence-forms.
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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 | 0.026 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.064 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".