Influences of Self-Efficacy, Response Efficacy, and Reactance on Responses to Cigarette Health Warnings: A Longitudinal Study of Adult Smokers in Australia and Canada
Bibliographic record
Abstract
Guided by the extended parallel process model (EPPM) and reactance theory, this study examined the relationship between efficacy beliefs, reactance, and adult smokers' responses to pictorial health warning labels (HWL) on cigarette packaging, including whether efficacy beliefs or reactance modify the relationship between HWL responses and subsequent smoking cessation behavior. Four waves of data were analyzed from prospective cohorts of smokers in Australia and Canada (n = 7,120 observations) over a period of time after implementation of more prominent, pictorial HWLs. Three types of HWL responses were studied: psychological threat responses (i.e., thinking about risks from smoking), forgoing cigarettes due to HWLs, and avoiding HWLs. The results from Generalized Estimating Equation models indicated that stronger efficacy beliefs and lower trait reactance were significantly associated with greater psychological threat responses to HWLs. Similar results were found for models predicting forgoing behavior, although response efficacy was inversely associated with it. Only response efficacy was significantly associated with avoiding HWLs, showing a positive relationship. Higher self-efficacy and stronger responses to HWLs, no matter the type, were associated with attempting to quit in the follow-up period; reactance was unassociated. No statistically significant interactions were found. These results suggest that stronger efficacy beliefs and lower trait reactance are associated with some stronger responses to fear-arousing HWL responses; however, these HWL responses appear no less likely to lead to cessation attempts among smokers with different levels of self-efficacy to quit, of response efficacy beliefs, or of trait reactance against attempts to control their behavior.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".