Donor attention to reading materials
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Mandatory predonation reading materials inform donors about risk factors for transmissible disease, possible complications of donation and changes to the donation process. We aimed to assess the attention to predonation reading materials and factors which may affect attention. MATERIALS AND METHODS: A national survey in 2008 of 18,108 blood donors asked about self-assessed attention to reading the materials. In face-to-face interviews, 441 donors completed additional questions about reading the materials and a literacy test. Qualitative interviews of 27 donors assessed their approach to reading. RESULTS: In the national survey, most of the first-time donors said they read all or most of the materials (90.9% first-time vs. 57.6% repeat donors, P < 0.001) and 66% vs. 23.1% reported reading them carefully (P < 0.001). In face-to-face interviews comparing those who read materials carefully, skimmed or did not read, most knew that donors are informed of positive transmissible disease test results (97.1%, 95.5, 98.0 P > 0.05), but fewer recalled seeing the definition of sex (77.2%, 56.9, 24.2 P < 0.001). Literacy was poor (30.5% frustration level, 60.3% instructional, 9.2% independent) but similar when those who read materials carefully, skimmed or did not read were compared (P > 0.05). Qualitative interviews showed that donors are reluctant to read any more than necessary and decide based on perceived importance or relevance. CONCLUSION: Attention to predonation reading materials tends to be better among first-time donors. The effectiveness is limited by low motivation to read, especially for repeat donors, as well as poor literacy.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".