Experience of blood donation and return for subsequent donation among new and repeat donors
Bibliographic record
Abstract
BACKGROUND: Understanding factors that influence return for subsequent donation in new compared to repeat blood donors is essential to recruiting and maintaining the blood donor base. STUDY DESIGN AND METHODS: The current research assessed self-reported attitudes, social norms, perceived behavioral control, intentions to donate, experiences during a current donation, and subsequent blood donation during the next 6 months, in a Canadian sample that included both new and repeat donors. RESULTS: Both new and repeat donors had similar positive impressions of a novel blood donor questionnaire (DQ) that assessed individual sexual risk behavior as a basis for donor eligibility. Attitudes, norms, and perceived behavioral control predicted intentions and sureness to donate in the next 6 months, and intentions/sureness to donate were moderately predictive of subsequent donation within this time frame. New donors (compared to repeat donors) and participants who indicated that some aspect of their donation day experience could prevent them from returning for a subsequent donation, were significantly less likely to make a future donation within the next 6 months. DISCUSSION: Findings provide guidance for the support of new and repeat donors returning for subsequent blood donation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".