Using the question-behavior effect to promote disease prevention behaviors: Two randomized controlled trials.
Bibliographic record
Abstract
OBJECTIVE: To test the efficacy of interventions based on the question-behavior effect in promoting the adoption of disease prevention behaviors. DESIGN: In Study 1, adults from the general public were randomly allocated to complete a questionnaire about health checks (question-behavior effect condition) or not (control) and later received an invitation to attend for screening. In Study 2, health care professionals were randomly allocated to complete a questionnaire about influenza vaccination or not and later had the opportunity to receive a vaccination. MAIN OUTCOME MEASURES: We objectively assessed health check attendance (Study 1) and influenza vaccination (Study 2). RESULTS: In Study 1, intention-to-treat analyses indicated that health check attendance was significantly higher in the question-behavior effect condition (68.3%) compared with the control condition (53.5%). In Study 2, intention-to-treat analyses indicated that influenza vaccination was significantly higher among participants in the question-behavior effect condition (42.0%) compared with the control condition (36.3%), and this effect persisted after controlling for demographic variables. Explanatory analyses indicated that the effects in both studies were attributable to completing rather than merely receiving the questionnaire and were stronger for those with positive attitudes or intentions about the target behavior. CONCLUSION: The question-behavior effect represents a simple, cost-effective means to increase disease prevention behaviors among the general public and health professionals. Implications for promoting health behaviors are discussed.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 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".