Effects of Patient Sexual Orientation and Gender Identity on Medical Students' Decision Making Regarding Preexposure Prophylaxis for Human Immunodeficiency Virus Prevention: A Vignette-Based Study
Bibliographic record
Abstract
INTRODUCTION: Preexposure prophylaxis (PrEP) is a pillar of our national strategy to end the human immunodeficiency virus (HIV) epidemic. However, one of the largest obstacles to realizing the effectiveness of PrEP is expansion of prescription to all patients at risk for HIV. In this vignette-based study, we sought to investigate medical students' decision making regarding PrEP by presenting fictional patients, all of whom had HIV risk factors based on sexual behavior. METHODS: We systematically varied patients' sexual orientation or gender identity (heterosexual female, gay male, bisexual male, transgender male, transgender female, gender nonbinary person). We assessed the medical students' willingness to prescribe PrEP to the patients, as well as their perceptions of the patients' HIV risk and behavior. RESULTS: A total of 670 US medical students completed the study. The heterosexual female patient was least frequently identified as a PrEP candidate, was viewed as least likely to adhere to PrEP, and the most likely to engage in condomless sex if prescribed PrEP; however, was considered at lower overall HIV risk. Lower perceived HIV risk and anticipated PrEP adherence were both associated with lower willingness to prescribe PrEP. Willingness to prescribe PrEP was highest for the gay male patient and lowest for the heterosexual female. CONCLUSIONS: These analyses suggest that assumptions about epidemiological risk based on patients' gender identity or sexual orientation may reduce willingness to prescribe PrEP to heterosexual women, ultimately hindering uptake in this critical population.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".