Donor's understanding of the definition of sex as applied to predonation screening questions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Predonation screening questions about sexual risk factors should provide an extra layer of safety from recently acquired infections that may be too early to be detected by testing. Donors are required to read a definition of sex as it applies to predonation screening questions each time they come to donate, but how well donors apply such definitions has not been evaluated. We aimed to determine how donors define sex when answering screening questions. MATERIALS AND METHODS: In total, 1297 whole blood donors were asked in a private interview to select from a list of sexual activities which ones they believed were being asked about in sexual background questions. Donors' definitions were coded as under-inclusive, correct or over-inclusive in relation to the blood services' definition. Qualitative interviews were carried out with 21 donors to understand reasoning behind definitions. RESULTS: Most donors had an over-inclusive definition (58.7%) or the correct definition (31.9%). Of the 9.4% of donors who had an under-inclusive definition, 95% included both vaginal and anal sex, but not oral sex. About 9% in each group were first-time donors (P > 0.05) who had never read the definition. The qualitative interviews indicated that donors reason their definition based on their own concept of transmissible disease risk. CONCLUSION: Donors apply a range of definitions of sex when answering questions about their sexual background. This may be due to different concepts of risk activities, and required reading of the definition has little impact.
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".