Nicotine Dependence as a Moderator of Message Framing Effects on Smoking Cessation Outcomes
Bibliographic record
Abstract
BACKGROUND: The persuasiveness of gain-framed and loss-framed messages for smoking cessation may vary by smokers' characteristics. Preliminary research in non-treatment-seeking smokers has shown that level of nicotine dependence moderates the effects of framed smoking messages on quit intentions and smoking cessation attitudes. Nicotine dependence as a potential moderator of message framing effects on actual smoking outcomes among treatment-seeking smokers remains to be determined. PURPOSE: This secondary analysis of data from a smoking cessation trial (Psychol Addict Behav. 2007; 21: 534-544) examined nicotine dependence as a moderator of message framing effects on smoking cessation success. METHODS: Dependence scores were dichotomized into high and low dependence (n = 249). RESULTS: Among high-dependent smokers, gain-framed messages were associated with higher levels of smoking abstinence both during and post-treatment than loss-framed messages. There was no differential effect of gain- versus loss-framed messages among low-dependent smokers. CONCLUSION: These preliminary findings suggest that the effectiveness of message framing interventions for treatment-seeking smokers may vary by smokers' level of nicotine dependence.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".