Comparing social norms for adolescent smoking and vaping behaviours using game theory based experiments and self-reports: insights from the MECHANISMS Study
Bibliographic record
Abstract
Background: Many adolescent smoking prevention programs target social norms, typically evaluated with self-report, susceptible to social desirability bias. An alternative approach with limited application in public health is to use experimental norms elicitation methods. Methods: Using the Mechanisms of Networks and Norms Influence on Smoking in Schools (MECHANISMS) study baseline data, from 12–13 year old school pupils (n=1656) in Northern Ireland and Bogotá, we compare two methods of measuring injunctive and descriptive smoking/ vaping norms. These include: (1) incentivized experiments, eliciting norms using monetary payments; (2) self-report scales. Confirmatory factor analysis (CFA) examined whether the methods measured the same construct. Paths from exposures (country, sex) to norms, and associations of norms with smoking behaviour/intentions were inspected in structural models. Results: Second-order CFA showed latent variables representing experimental and survey norms measurements were measuring the same underlying construct of anti-smoking/vaping norms. Adding covariates into structural models showed significant paths from country to norms (second-order anti-smoking/vaping norms latent variable: standardized factor loading [β]=0.30, standard error [SE]=0.09, p < 0.001), and associations of norms with self-reported anti-smoking behaviour (β = 0.40, SE = 0.04,p < 0.001), anti-smoking intentions (β = 0.42, SE = 0.06, p < 0.001), and objectively measured smoking behaviour (β = −0.20, SE = 0.06, p = 0.001).Conclusions and implications: We provide evidence for the construct validity of behavioural economic methods of eliciting adolescents moking/vaping norms. These methods seem to index the same underlying phenomena as commonly-used self-report scales. Our research uses innovative, transdisciplinary insights from game theory about norms elicitation that will have future relevance for other health-related behaviours.
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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 | 0.028 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".