An adapted postdonation motivational interview enhances blood donor retention
Bibliographic record
Abstract
BACKGROUND: Blood donors may hold conflicting thoughts about future donation. While they may perceive the direct benefit to themselves and others, they often report compelling reasons not to give again. As a result the standard encouragements to return may not be sufficient to motivate some donors. This study examined the effects of a postdonation adapted motivational interview (AMI) on blood donor attitudes and repeat donation behavior. STUDY DESIGN AND METHODS: Donors (n = 215) were randomly assigned to either an AMI or a no-interview control group. Approximately 1 month after their index donation, donors in the AMI group completed a brief telephone interview to clarify individual-specific motivations and values concerning blood donation and address potential barriers. They were then asked to complete questionnaires regarding donation attitudes, anxiety, self-efficacy, and intention to donate. Donors in the control group were also contacted 1 month after donation and asked to complete the same series of questionnaires. RESULTS: Donors in the AMI group reported greater intention to provide a future donation (F = 8.13, p < 0.05), more positive donation attitudes (F = 4.59, p < 0.05), and greater confidence in their ability to avoid adverse reactions (F = 10.26, p < 0.01). Further, AMI was associated with higher rates of attempted donation at 12 months (odds ratio, 2.48; 95% confidence interval, 1.27-4.87). CONCLUSION: Application of an AMI may be an effective strategy to increase the donor pool by enhancing retention of existing donors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".