Using a protection motivation theory framework to reduce vaping intention and behaviour in Canadian university students who regularely vape: A randomized controlled trial
Bibliographic record
Abstract
Using Protection Motivation Theory (PMT), we examined the effect of threat appraisal information (perceived vulnerability-PV and perceived severity-PS) to reduce vaping intentions, and in turn reduce vaping use. Canadian university students ( n = 77) who vape regularly were randomized to either PMT or attention control treatment conditions. Data were collected at baseline and 3 time points after the intervention: Day 7, Day 30, and Day 45. Participants assigned to the PMT group showed significant increases in PV, PS, and intentions to vape less ( p ⩽ 0.05) compared to those in the attention control group. Less convincing evidence was found between treatment groups for vaping use. PS and PV predicted vaping intentions, whereas vaping intentions did not predict vaping use. It is suggested through this study that the threat appraisal components of PMT can be successfully manipulated to reduce the intentions to vape and to a lesser extent reduce vaping use among University vapers.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".