Assessing unintentional creation of bias against men who have sex with men as a function of exposure to blood donor screening questionnaire: A national randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Canadian Blood Services (CBS) screens donors based on group status (e.g., men who have sex with men, MSM) instead of specific, high-risk sexual practices (e.g., occurrence of condomless sex). The MSM screening question is embedded in a cluster of questions about stigmatized attributes such as history of imprisonment and illicit substance use. This juxtaposition of the "MSM question" and stigmatized attributes may unintentionally cause blood donors to perceive MSM more negatively. The aim of this research is to determine whether the CBS donor eligibility questionnaire generates negative bias against MSM. STUDY DESIGN AND METHODS: A national, randomized online study of 903 CBS donors was conducted. Participants completed either the existing blood donor eligibility questionnaire or a modified donor questionnaire that repositioned the MSM question among neutral questions. After completing the existing or modified questionnaire, bias against MSM was measured using the sexuality implicit association test (IAT) and Modern Homonegativity Scale - Gay Men (MHS-G). Lastly, participants estimated prevalence rates among MSM of certain stigmatized behaviors. RESULTS: = .506) and prevalence estimation task (p = .443) indicated that question order had no significant impact on explicit bias. DISCUSSION: Positioning the MSM screening question among stigmatizing questions creates implicit negative bias against MSM. Policy makers should be mindful of question positioning when designing donor questionnaires.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".