The effects of a phone call prompt on subsequent blood donation among first‐time donors
Bibliographic record
Abstract
BACKGROUND: A strategy used by blood centers to retain donors is to place phone call reminders. However, among first-time donors, no studies have tested the effect of this strategy. This was the aim of this study among individuals who had recently given their first lifetime blood donation. STUDY DESIGN AND METHODS: A quasi-experimental study using a nonequivalent control group was adopted; participants in the control group were donors with blood types B+ and AB+, since these individuals are not phoned by the blood agency. A total of 1604 first-time donors aged 18 to 70 years from the province of Quebec, Canada, were assigned to the experimental (n = 870) or the control (n = 734) group. Participants in the experimental group were phoned a few days before they had a new opportunity to give blood while those in the control condition were not phoned. RESULTS: In the experimental condition, 48.3% of the donors attempted to give blood during the 12-month follow-up period compared to 38.0% in the control condition. The hazards of the first blood donation attempt among donors who were phoned were 32% higher compared to the hazards of those who were not phoned (p = 0.0004). CONCLUSION: The results of this study indicate that a first phone call reminder about the upcoming opportunity to give blood again has a significant positive effect on return rates among first-time donors.
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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 | 0.012 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".