Perceptions of donor screening—Do I always need to tell the truth?
Bibliographic record
Abstract
BACKGROUND: The Donor Health Assessment Questionnaire (DHQ) is fundamental to blood safety. We describe attitudes towards truthfulness among first-time donors who tested positive for transfusion transmissible infections and those who did not. METHODS AND MATERIALS: From 2005 to 2022 donors positive for infectious markers (cases) and demographically matched controls rated their agreement with statements about truthfulness, privacy and the value of the DHQ. RESULTS: There were 798 (32% participation) cases and 3192 (39% participation) controls. Most said they read questions carefully (93% cases, 96% controls, p < 0.01) and answered truthfully (95% cases, 99% controls p < 0.01). Fewer thought the questions make the blood safer (79% cases, 80% controls, p = 0.39) and some agreed it is OK not to answer questions truthfully if you know your blood is safe (21% cases, 16% controls, p < 0.01). Privacy to answer personal questions was generally adequate (88% cases, 91% controls, p < 0.01). Attitudes were similar regardless of paper or electronic DHQ format. CONCLUSION: Most first time donors believe they answer screening questions truthfully, but some question the safety benefit to recipients and judge whether they need to be truthful. This was true for donors with positive infectious markers as well as their matched infection-negative controls.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".