Sexual risk-taking: Sexually transmitted infections and the presence of amplified sexual stigma
Bibliographic record
Abstract
Despite the high prevalence of sexually transmitted infections (STIs) in our society and their increasing rates over the past decade, stigma continues to be harmful and pervasive. This study examines the perceptions of STIs and their implications for risk and social perceptions. Prolific participants ( N = 440) read one of six vignettes involving a sexual encounter in which a target unknowingly transmitted either a sexual illness (STI) or a non-sexual illness (H1N1) that varied in severity (moderate, severe, fatal) to another person. Targets who transmitted a sexual illness were rated as riskier, more negative overall, and more selfish, regardless of illness severity. In line with flawed risk evaluations, participants did not distinguish between moderate and severe STIs for both risk and interpersonal perceptions. All dependent variables demonstrated that STIs were viewed more negatively than non-sexual illnesses of an equal or greater severity. This study shows that the stigmatization of STIs is beyond their degree of severity, and this stigma produces damaging interpersonal perceptions and elevated risk assessments. Implications center around the need for continued stigma reduction and interventions to improve evaluations of risk.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".