Improving blood donor recruitment and retention: integrating theoretical advances from social and behavioral science research agendas
Bibliographic record
Abstract
BACKGROUND: Increasing blood donor recruitment and retention is of key importance to transfusion services. Research within the social and behavioral science traditions has adopted separate but complementary approaches to addressing these issues. This article aims to review both of these types of literature, examine theoretical developments, identify commonalities, and offer a means to integrate these within a single intervention approach. STUDY DESIGN AND METHODS: The social and behavioral science literature on blood donor recruitment and retention focusing on theory, interventions, and integration is reviewed. RESULTS: The role of emotional regulation (anticipated anxiety and vasovagal reactions) is central to both the behavioral and the social science approaches to enhancing donor motivation, yet although intentions are the best predictor of donor behavior, interventions targeting enactment of intentions have not been used to increase donation. Implementation intentions (that is, if-then plans formed in advance of acting) provide a useful technique to integrate findings from social and behavioral sciences to increase donor recruitment and retention. CONCLUSION: After reviewing the literature, implementation intention formation is proposed as a technique to integrate the key findings and theories from the behavioral and social science literature on blood donor recruitment and retention.
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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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".