The Effectiveness of the Protection Motivation Theory in Reducing Vaping Behaviour in a Student Population
Bibliographic record
Abstract
A threat appraisal model grounded in Protection Motivation Theory (PMT) determined whether providing health information regarding perceived severity (PS) and perceived vulnerability (PS) of vaping health complications, corroborates with reduced vaping-related intention and habitual behaviour. Seventy-seven regular vapers (22.21 ± 3.47 years) registered within a Canadian university were randomized into one of two groups, wherein the aforementioned threat appraisal information was present. Participants in the experimental group (n = 41) watched an 8-minute information video a week after baseline, following the threat appraisal components of PMT. Those in the attention control group (n = 36) watched an information video on nutrition and healthy lifestyle. Data were collected for PV, PS, vaping intentions, and vaping behaviour at baseline and the following 3 time points after the intervention: Day-7, Day-30, and Day-45. A complete (n = 77) and imputed (n = 416) analysis for missing data revealed a significant treatment group by time interaction effect for PV and PS. Specifically, those in the experimental group reported higher PV and PS scores, compared to their attention control counterparts. For vaping intention, the treatment group by time interaction effect was significant for imputed but not complete data. In both analyses, intentions to vape less, particularly immediately after the intervention was evident in the experimental group but not in the attention control group. For vaping behavior, the treatment group by time interact effect was significant for imputed but not complete data. In both analyses, vaping use after the intervention dropped for both groups, however the drop for those in the experimental group was more pronounced than the drop for those in the attention control group. Both PS and PV were correlated with vaping intention at all assessment time points. Specifically, higher PS and PV scores were associated with intentions to vape less. Weak and inconsistent evidence was provided that intentions to vape less is correlated with actually vaping less. It is suggested through this study that the threat appraisal components of PMT (i.e., PS and PV) can be successfully manipulated among University vapers, which in turn can reduce their intentions to vape and to a lesser extent reduce their actual vaping use. Implications for future vaping intervention research within a public health education framework, are discussed.
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.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".