Perceptions of Sexual Partner Safety
Bibliographic record
Abstract
BACKGROUND: Many individuals select sexual partners based on assumed partner STI/HIV safety, yet few studies have investigated how these assumptions are formed. The objective of this research was to determine the extent to which partner safety beliefs were used to evaluate partner safety, and whether these beliefs influenced perceptions of personal STI/HIV risk. METHODS: Participants (n = 317) recruited from an STI clinic completed a structured self-report questionnaire. A Partner Safety Beliefs Scale (PSBS) was developed to determine the factors that most influenced perceived partner safety. Exploratory factor analysis showed that a single factor accounted for 46% of the variance in the PSBS; with an internal consistency of 0.92. Linear regression was used to determine factors predictive of perceived personal STI/HIV risk. RESULTS: Participants endorsed statements indicating that knowing or trusting a sexual partner influences their beliefs about their partner's safety. Linear regression analysis indicated that education, income, number of sexual partners, and PSBS scores were significant predictors of perceived personal STI/HIV risk. CONCLUSIONS: The results of this study indicate that many individuals are relying on partner attributes and relationship characteristics when assessing the STI/HIV status of a sexual partner, and that this reliance is associated with a decreased perception of personal STI/HIV risk. Prevention campaigns need to acknowledge that people are likely to evaluate sexual partners whom they know and trust as safe. Dispelling erroneous beliefs about the ability to select safe partners is needed to promote safer sexual behavior.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".