On the Importance of Unconditional Rewards for Blood Donations
Bibliographic record
Abstract
To the Editor: In commenting on our article, “Economic Rewards to Motivate Blood Donations,” which appeared in Science in May 2013 (1), Kreuter and Gandhi argue that the subtle differences between “a token of appreciation and something with transferable cash value” could have important distinct effects on donations (2). They also posit that by offering rewards, donors have an incentive to lie on eligibility surveys to receive the rewards. Although it is theoretically possible that a token of appreciation and something with transferable cash value could have distinct effects on blood donation behavior (but no field study has yet directly addressed this question), it cannot explain the evidence we presented in our article (1). In every case that we studied, potential donors were always given rewards before being administered any questionnaire for eligibility. That is, the rewards were unconditional on making a donation, so there was no incentive to lie on the eligibility interview solely to receive the rewards. We explicitly mention this important detail in our article, highlighting that such unconditionality might indeed be critical for blood safety. Moreover, unconditional rewards are the standard and, as our article shows, effective practice adopted by the major blood collection organizations.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.066 | 0.065 |
| Insufficient payload (model declined to judge) | 0.005 | 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".