Translational Science: Basic Science to Public Policy and Back Again
Bibliographic record
Abstract
In this month’s issue of Nicotine & Tobacco Research, the overarching theme is translational science in tobacco control. To set the stage there is an excellent review from the SRNT Basic Science Network1 on the ways in which findings from basic science research has informed and shaped public policy and how bidirectional communication between basic scientists and policy makers can further improve the translation of knowledge into policy. Given the current landscape of an ever-increasing variety of tobacco and nicotine products such as heat-not-burn, very low nicotine cigarettes (VLNCs) and other alternative nicotine delivery systems, there is a need for both tobacco research and policy to be nimble in order to adapt and implement relevant evidence-based policies. Next there are several papers assessing the effectiveness of cigarette package warnings as a public health policy. Lazard et al.2 present findings on believability of cigarette warnings regarding the addictive potential of nicotine in cigarettes and menthol as a contributor to the addictive potential of cigarettes. They found that the majority of adolescents and adults believe that both cigarettes and nicotine are addictive but are less likely to believe that menthol cigarettes are more addictive than regular cigarettes. This supports the policy of adding warning labels regarding addictiveness. Four studies assessing Graphic Warning Labels (GWLs) report similar findings. In a study by Cochran et al.3 neural processing of GWLs and how this affects attentional bias towards smoking cues was assessed using EEG. Their findings indicate that anxiety-provoking GWLs actually increase attentional bias to smoking cues while GWLs that elicit disgust has the opposite effect. They conclude that GWLs that are disgust-focused may be a better public health strategy for encouraging cessation attempts among current smokers. The second study also assessed attentional bias but researchers were interested in whether the size of the GWLs themselves altered visual attention and negative affect and intentions to quit.4 They found that GWLs that covered 50% of the package were more effective than GWLs covering 30% of the package at increase visual attention and negative affect. Intentions to quit were also higher among smokers exposed to the 50% GWL than those not exposed to a GWL, while those exposed to 30% GWL did not differ from controls on quit intention. The authors conclude that larger GWLs may be more effective, although courts in the United States have blocked this. The third study by Morgan et al.5 demonstrated that GWLs were more effective than text only warnings in sparking conversations among smokers social networks regarding the health effects of smoking and quitting than those exposed to text only warnings. A study comparing responses to GWLs over time in both Canada and Australia6 demonstrates that while attention to GWLs decreased over a 2-year period, cognitive responses increased especially in higher SES smokers, again demonstrating the overall effectiveness of the policy. These findings are confirmed in another study in North Carolina7 showing that while emotional and cognitive reactions to GWLs may wane over time, quit intentions actually increase. These studies taken together demonstrate that GWLs have the desired effect on smokers and as such are an effective public policy cessation intervention.
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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 | 0.097 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.024 | 0.046 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.052 | 0.049 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".