The donor health assessment questionnaire: potential for format change and computer‐assisted self‐interviews to improve donor attention
Bibliographic record
Abstract
BACKGROUND: The Canadian donor health assessment questionnaire (DHAQ) has developed gradually over many years. The purpose of this study was to determine whether the format and method of administration of the DHAQ influences donor attentiveness. STUDY DESIGN AND METHODS: Between May 2004 and September 2005, a total of 1,397 donors participated in the study by completing the DHAQ by one of three methods: the current Canadian Blood Services (CBS) format and method of administration, which is partly self-administered and partly interviewer-administered (Method 1); the DHAQ reformatted to the AABB Uniform Donor Health Questionnaire format and self-administered (Method 2); and an audiovisual computer-assisted self-interview (CASI; Method 3). This was followed by a short, scripted interview assessing recall of 17 specific items queried on the DHAQ. Time to completion of the DHAQ and degree of familiarity with computer use were also assessed. RESULTS: The percentages of donors identifying all 17 items correctly were 9.4, 20.9, and 34.8 percent and the mean percentages recall of items were 53.9, 56.0, and 69.8 percent with DHAQ administration Methods 1, 2, and 3, respectively (p < 0.0001). This difference was largely attributable to the poor recall of items queried as part of a list in Method 1. Mean times to complete the DHAQ were 3.1, 3.8, and 8.1 minutes for Methods 1, 2, and 3, respectively. More than 95 percent of donors had used a personal computer in the past year. CONCLUSION: The current format of the Canadian DHAQ is not optimal for donor attention to specific questions asked as part of a list. Attention was improved by use of AABB uniform donor history questionnaire format and was best with use of a CASI format.
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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 | 0.067 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".