A Content Analysis of Behavior Change Techniques Employed in North American Vaping Prevention Interventions
Bibliographic record
Abstract
Introduction: Vaping among North American youth has surfaced as a concerning public health epidemic. Increasing evidence of harms associated with E-cigarette use, especially among the young, has prompted urgency in addressing vaping. Although a number of individual behavior change campaigns have arisen as a result, little is known about which behavior change techniques are being employed to influence youth vaping behavior. In this study, we aimed to code all North American vaping prevention campaigns using the behavior change technique taxonomy (Version 1) to determine which behavior change techniques are being used. Methods: We identified the sample of campaigns through systematic searches using Google. After applying the exclusion criteria, the campaigns were reviewed and coded for behavior change techniques. Results: In total, 46 unique vaping prevention campaigns were identified, including 2 federal (1 from Canada, 1 from the U.S.), 43 U.S. state-level, and 1 Canadian provincial-level campaign(s). The number of behavior change technique categories and behavior change techniques in a campaign ranged from 0 to 5 (mean=1.56) and 0 to 6 (mean=2.13), respectively. Of the 16 possible behavior change technique categories, 4 were utilized across the campaigns, which included 5. Natural consequences (89%), 6. Comparison of behavior (22%), 13. Identity (20%), and 3. Social support (11%). Conclusions: Only a small number of behavior change techniques were used in North American vaping prevention campaigns, with a heavy and often sole reliance on communicating the health consequences of use. Incorporating other promising behavior change techniques into future campaigns is likely a productive way forward to tackling the complex and multifaceted issue of youth vaping.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".