Alcohol use as a risk factor for sexually transmitted infections: A systematic review and conclusions for prevention
Bibliographic record
Abstract
Alcohol use has been associated with multiple types of sexual risk behaviors, such as condomless sex or having multiple sexual partners, behaviors that are linked to the risk of sexually transmitted infections (STIs). The aim of this review was to present updated evidence to demonstrate an association between alcohol consumption and STIs and evaluate the causal nature of this link, as well as to present interventions that reduce alcohol consumption and its effect on STIs. We conducted a systematic review according to the PRISMA guidelines using PubMed and Embase databases. Cohort studies and case-control studies were included. Any level of alcohol use served as the exposure variable, with the outcome restricted to non-HIV STIs, as reviews on alcohol use and HIV already exist. In total, 11 publications satisfied the inclusion criteria. The evidence suggests that there is an association between alcohol use, especially heavy drinking occasions, and STIs, with eight articles finding a statistically significant association. In addition to these results, there is indirect causal evidence from policy studies, and from the field of decision-making and sexual behavior with experimental evidence, that alcohol use increases the likelihood of risk-taking sexual behavior. It is important to have a deeper understanding of the association to develop effective prevention programs at community and individual levels. Preventive interventions should be implemented targeting the general population, in addition to specific campaigns directed at vulnerable subpopulations in order to reduce the risks.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".