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Record W2555108844 · doi:10.1080/10463283.2016.1245940

The question-behaviour effect: A theoretical and methodological review and meta-analysis

2016· review· en· W2555108844 on OpenAlex
Sarah Wilding, Mark Conner, Tracy Sandberg, Andrew Prestwich, Rebecca Lawton, Chantelle Wood, Eleanor Miles, Gaston Godin, Paschal Sheeran

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Review of Social Psychology · 2016
Typereview
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversité Laval
FundersEconomic and Social Research Council
KeywordsPsychologyFluencyCognitive dissonanceSocial psychologyMeta-analysisMathematics educationCognitive psychology

Abstract

fetched live from OpenAlex

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; (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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysislow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysishigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.875
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.004
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.280
GPT teacher head0.588
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it