Face‐to‐face interviewing in predonation screening: lack of effect on detected human immunodeficiency virus and hepatitis C virus infections
Bibliographic record
Abstract
BACKGROUND: Predonation screening has become more elaborate over the years, while human immunodeficiency virus (HIV)- and hepatitis C virus (HCV)-positive donations have declined. The impact of face-to-face interviewing and of the format of the Donor Health Assessment Questionnaire (DHAQ) have not been evaluated. STUDY DESIGN AND METHODS: Canadian Blood Services DHAQ records between 1990 and 2004 were examined, and changes in them were tracked. The proportion of first-time donors permanently deferred for HIV or HCV risk, and the HIV and HCV rates per 100,000 donations, were calculated annually. Time-series analysis was used to determine whether major predonation screening changes had any effect on the HIV or HCV rates or permanent deferrals. RESULTS: In 1992, receiving money or drugs for sex was added to the DHAQ; otherwise, the content of high-risk questions changed little between 1990 and 2004. In 1997, the method of administration of the DHAQ changed from donor-completed to face-to-face interviewing for high-risk questions. Permanent deferrals for HIV or HCV risk factors and HIV and HCV rates in first-time donors decreased over this period. The HIV rates were close to 0 before 1997, whereas HCV rates decreased steadily through 2004. There was no interruption in rates in 1997 when the method of administration changed. CONCLUSION: Face-to-face interviewing for high-risk questions had no effect on HIV or HCV rates in first-time donations over 15 years of observation (during the latter 8 of which face-to-face interviewing was in place), and it did not increase permanent deferrals for HIV or HCV risk factors.
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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 | 0.125 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".