Does Reactance against Cigarette Warning Labels Matter? Warning Label Responses and Downstream Smoking Cessation amongst Adult Smokers in Australia, Canada, Mexico and the United States
Bibliographic record
Abstract
OBJECTIVE: Some researchers have raised concerns that pictorial health warning labels (HWLs) on cigarette packages may lead to message rejection and reduced effectiveness of HWL messages. This study aimed to determine how state reactance (i.e., negative affect due to perceived manipulation) in response to both pictorial and text-only HWLs is associated with other types of HWL responses and with subsequent cessation attempts. METHODS: Survey data were collected every 4 months between September 2013 and 2014 from online panels of adult smokers in Australia, Canada, Mexico, and the US were analyzed. Participants with at least one wave of follow-up were included in the analysis (n = 4,072 smokers; 7,459 observations). Surveys assessed psychological and behavioral responses to HWLs (i.e., attention to HWLs, cognitive elaboration of risks due to HWLs, avoiding HWLs, and forgoing cigarettes because of HWLs) and cessation attempts. Participants then viewed specific HWLs from their countries and were queried about affective state reactance. Logistic and linear Generalized Estimating Equation (GEE) models regressed each of the psychological and behavioral HWL responses on reactance, while controlling for socio-demographic and smoking-related variables. Logistic GEE models also regressed having attempted to quit by the subsequent survey on reactance, each of the psychological and behavioral HWL responses (analyzed separately), adjustment variables. Data from all countries were initially pooled, with interactions between country and reactance assessed; when interactions were statistically significant, country-stratified models were estimated. RESULTS: Interactions between country and reactance were found in all models that regressed psychological and behavioral HWL responses on study variables. In the US, stronger reactance was associated with more frequent reading of HWLs and thinking about health risks. Smokers from all four countries with stronger reactance reported greater likelihood of avoiding warnings and forgoing cigarettes due to warnings, although the association appeared stronger in the US. Both stronger HWLs responses and reactance were positively associated with subsequent cessation attempts, with no significant interaction between country and reactance. CONCLUSIONS: Reactance towards HWLs does not appear to interfere with quitting, which is consistent with its being an indicator of concern, not a systematic effort to avoid HWL message engagement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".