Why Do Blood Donors Lapse or Reduce Their Donation's Frequency?
Bibliographic record
Abstract
Finding effective ways to retain blood donors is crucial. This study seeks to compare, in a context of a voluntary and nonremunerated system, donor demographics and deterrents to blood donation among plasma/platelet donors (PPDs), regular whole blood donors (WBDs), and lapsed whole blood donors (LWBD). Among 1879 participants to a survey on motivations, time use, and blood donation, 207 WBDs (26%) and 148 PPDs (31%) said that they reduced their donation frequency over the last 5 years. Participants to this survey also included 609 LWBDs, who did not donate in the past 5 years. We asked about reasons why they reduce or cease to donate blood and demographic variables. χ(2) Tests were completed to determine which deterrents stand out across the 3 blood donor groups. The deterrent indicating the highest percentage was "time constraints related to work or studies" (43% for all respondents). Comparison of WBDs, LWBDs, and PPDs shows that results for 7 deterrents were statistically different between the 3 groups. Obstacles to donating blood also vary based on sex, age (life course), and level of education. Blood collection agencies should consider developing new retention strategies tailored to blood donors, taking into account the specific profiles of female/male donors, events that typically occur at various stages of life, and particular challenges associated with differences in levels of education.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".