Validation of new, gender‐neutral questions on recent sexual behaviors among plasma donors and men who have sex with men
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Several blood services might eventually interview donors with gender-neutral questions on sexual behaviors to improve the inclusivity of blood donation. We tested two ways (i.e., "scenarios") of asking donors about their recent sexual behaviors. MATERIALS AND METHODS: The study comprised 126 regular source plasma donors and 102 gay, bisexual, and other men who have sex with men (gbMSM), including 73 cis-gbMSM (i.e., the "cis-gbMSM subgroup," which excluded nonbinary, genderqueer, and trans individuals). In Scenario 1, participants were asked if, in the last 3 months, they "have […] had a new sexual partner or more than one sexual partner." In Scenario 2, they were asked "Have you had a new sexual partner?" and "have you had more than one sexual partner?". Validation questions included more specific questions on the type of partners and sexual activity. RESULTS: Among plasma donors, sensitivity was 100.0% for both scenarios; specificity was 100.0% and 99.1% for Scenarios 1 and 2, respectively. Among gbMSM, sensitivity was 74.5% and 82.9% for Scenarios 1 and 2, respectively; specificity was 100.0% for both scenarios. Among cis-gbMSM, sensitivity was 88.6% and 100.0% for Scenarios 1 and 2, respectively; specificity was 100.0% for both scenarios. The area under the receiver operating characteristic curve of Scenario 2 was significantly higher than that of Scenario 1 among gbMSM and in the cis-gbMSM subgroup (all p < .05). CONCLUSION: Scenario 2 questions performed well among plasma donors and cis-gbMSM, but less so in the broader gbMSM population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".