Promoting the return of lapsed blood donors: A seven-arm randomized controlled trial of the question–behavior effect.
Bibliographic record
Abstract
OBJECTIVE: This study tested key variations in the question-behavior effect against a control condition or an implementation intention condition on returning to give blood among lapsed donors (individuals who had not given blood in the past 2 years). DESIGN: At baseline, 7,000 lapsed donors were randomized to 1 of 6 experimental conditions or to a control condition. Participants in the experimental conditions were asked to complete a 6-item postal questionnaire assessing intentions only, interrogative intention, moral norm plus intention, anticipated regret plus intention, positive self-image plus intention, or implementation intentions. OBJECTIVE measures of behavior were obtained 6 and 15 months later. The frequency of registrations to give blood over the next 6 and 15 months was measured. RESULTS: Intention-to-treat analysis of the frequency of registrations (GENMOD procedure, Poisson distribution) indicated main effects for condition (experimental vs. control) at both 6 months, χ²(1) = 4.64, p < .05, and 15 months, χ²(1) = 5.88, p < .05. Positive self-image and implementation intention interventions outperformed the control condition at 6 months. At 15 months, standard intention, interrogative intention, and regret plus intention conditions showed more frequent registrations compared with control and were just as effective as implementation intention formation. Moderation analysis showed that the moral norm and positive self-image conditions were significant for first-time (1 previous donation) but not repeat (2 or more previous donations) donors. CONCLUSION: The question-behavior effect can be used to reinvigorate blood donation among lapsed donors, and can be as effective as forming implementation intentions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".