The question-behaviour effect: A theoretical and methodological review and meta-analysis
Bibliographic record
Abstract
Research has demonstrated that asking people questions about a behaviour can lead to behaviour change. Despite many, varied studies in different domains, it is only recently that this phenomenon has been studied under the umbrella term of the question-behaviour effect (QBE) and moderators of the effect have been investigated. With a particular focus on our own contributions, this article: (1) provides an overview of QBE research; (2) reviews and offers new evidence concerning three theoretical accounts of the QBE (behavioural simulation and processing fluency; attitude accessibility; cognitive dissonance); (3) reports a new meta-analysis of QBE studies (k = 66, reporting 94 tests) focusing on methodological moderators. The findings of this meta-analysis support a small significant effect of the QBE (g = 0.14, 95% CI = 0.11, 0.18, p < .001) with smaller effect sizes observed in more carefully controlled studies that exhibit less risk of bias and (4) also considers directions for future research on the QBE, especially studies that use designs with low risk of bias and consider desirable and undesirable behaviour separately.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.041 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".