Evaluation of donor informed consents and associated <scp>predonation</scp> educational materials in the <scp>United States</scp> and Canada: variability in elements of consent and measures of readability and reading burden
Bibliographic record
Abstract
BACKGROUND: Every day, approximately 30,000 donors present to blood collection establishments in the United States or Canada, where they are provided information about donation and asked to sign a consent before donating. We evaluated elements of informational and consent documents and measures of readability that may influence their comprehension. MATERIALS AND METHODS: Consents for whole blood (WB) and automated collections and predonation reading materials (PRMs) representing over 93% of WB collections in the United States and Canada were evaluated. Elements, including risks of donation, were cataloged. Word count, Flesch-Kinkaid (F-K) reading ease/grade level scores, Simple Measure of Gobbledygook grade, and percentage of complex words were measured. RESULTS: F-K grade levels ranged from 9.2 to 16.9 for WB consents, 7.8 to 16.0 for apheresis consents, and 6.7 to 10.9 for PRMs, above the recommended level of eighth grade or lower for general audiences. F-K reading ease scores were below the cutoff of 60 for readability. Reading burden was substantial, with word count ranging from 131 to 885, 131 to 996, and 649 to 2743 for WB and apheresis consents and PRMs, respectively. Use of jargon and the absence of consent elements such as confidentiality, voluntariness, ability to withdraw consent, and risks of deferral were common. CONCLUSIONS: Donor consent documents and associated materials vary widely, are written at challenging grade levels, present considerable reading burden, contain substantial jargon, and are missing key elements of consent. The authors recommend an organized effort, including blood donors, legal experts, and blood collection experts, to reach consensus on the minimal requirements for standardized clear and concise consent documents in an optimized format.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".