Implementation intentions intervention among temporarily deferred novice blood donors
Bibliographic record
Abstract
BACKGROUND: Approximately 12% to 15% of blood donors are temporarily deferred from giving blood because they fail relevant medical criteria. Temporary deferral has a profound negative impact on subsequent donation. To our knowledge, an implementation intentions intervention has never been tested among temporarily deferred donors. We hypothesized that return rates would be higher among participants exposed to an implementation intentions intervention compared to those in a control condition that is mere measurement of related cognitions. STUDY DESIGN AND METHODS: Participants were assigned to implementation intentions or mere measurement conditions; whether or not participants received a temporary interdiction on giving blood was measured. A total of 956 novice donors were temporarily deferred (n = 490, in the implementation intentions condition; n = 466, in the mere measurement condition). Participants in the implementation intentions condition formed if-then plans to overcome three common barriers to blood donation: forgetting to attend, fitting the opportunity to give blood into one's schedule, and organizing transportation to the donation venue. Participants in the mere measurement condition did not form plans. RESULTS: Participants in the implementation intentions condition had a 19% greater chance of returning to give blood again within the 4 years after their first lifetime donation compared to participants in the mere measurement condition (p = 0.04) when controlling for sex and attempt at which participants were deferred. CONCLUSION: The results of this study indicate that implementation intentions could be a useful strategy for promoting donor return among temporarily deferred novice blood 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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".