Which survey questions change behavior? Randomized controlled trial of mere measurement interventions.
Bibliographic record
Abstract
OBJECTIVE: Evidence indicates that receiving a questionnaire about a behavior increases the likelihood that the person will perform that behavior--a phenomenon termed the mere measurement effect. This research tested the role of (a) the type of questions, and (b) questionnaire completion in optimizing the impact of mere measurement interventions designed to retain novice blood donors. DESIGN: Novice blood donors (N = 4391) were randomly allocated to four conditions that varied the content of a questionnaire about blood donation (behavioral intention-only, behavioral intention plus regret, implementation intention-only, implementation intention plus regret) or to a no-questionnaire control condition. MAIN OUTCOME MEASURES: Objective measures of registration at blood drives were obtained at 6 and 12 months postbaseline. RESULTS: Participants in the implementation intention-only condition donated more frequently at 6 months compared to participants in each of the other conditions. At 12 months both implementation intention conditions outperformed the other conditions. Implementation intentions increased the frequency of donations over 1 year by 12%. Measuring anticipated regret did not augment the impact of interventions whereas questionnaire completion had an important impact on donation behavior. CONCLUSION: Questions about implementation intentions but not behavioral intentions promote retention of 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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".