Varying cognitive targets and response rates to enhance the question-behaviour effect: An 8-arm Randomized Controlled Trial on influenza vaccination uptake
Bibliographic record
Abstract
RATIONALE: The question-behaviour effect (QBE) refers to the finding that survey questions about a behaviour can change that behaviour. However, little research has tested how the QBE can be maximized in behavioural medicine settings. The present research tested manipulations of cognitive targets (questions about anticipated regret or beneficence) and survey return rates (presence vs. absence of a sticky note requesting completion of the questionnaire) on the magnitude of the QBE for influenza vaccination in older adults. METHOD: Participants (N = 13,803) were recruited from general practice and randomly allocated to one of eight conditions: control 1 (no questionnaire); control 2 (demographics questionnaire); intention and attitude questionnaire (with or without a sticky note); intention and attitude plus anticipated regret questionnaire (with or without a sticky note); intention and attitude plus beneficence questionnaire (with or without a sticky note). Objective records of subsequent influenza vaccination from general practice records formed the dependent variable. RESULTS: Intention-to-treat analyses indicated that receiving an influenza vaccination questionnaire significantly increased vaccination rates compared to the no questionnaire, OR = 1.17, 95% CI = 1.01, 1.36 and combined control conditions, OR = 1.13, 95% CI = 1.01, 1.25. Including the sticky note significantly increased questionnaire return rates, OR = 1.25, 95% CI = 1.04, 1.50. However, there were no differences in vaccination rates between questionnaires containing different cognitive targets, a sticky note or not, and no interactions. There were no significant differences in the per-protocol analyses, i.e. among respondents who completed and returned the questionnaires. CONCLUSION: The QBE is a simple, low-cost intervention to increase influenza vaccination rates. Increasing questionnaire return rates or asking anticipated regret or beneficence questions in addition to intention and attitude questions did not enhance the QBE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".