Developing tobacco risk communications for young adults susceptible to dual use of combustible cigarettes and nicotine vapes (Preprint)
Bibliographic record
Abstract
Background: Dual use of combustible cigarettes and nicotine vapes is disproportionately high among lesbian, gay, bisexual, transgender, and queer (LGBTQ+) young adults. Mass-reach health communications may be effective at curbing dual use. Current research is exploring whether comparative risk messaging, which presents nicotine vapes as less harmful than cigarettes, reduces dual use. Objective: This formative message testing study focused on communicating the health risks of cigarette smoking and nicotine vaping use to young adults susceptible to dual use of these products, including LGBTQ+ young adults. Methods: Online focus groups were conducted with young adults to develop candidate messages (N=12). Interviews (N=13) qualitatively explored thematic content. An online rating survey (N=286) quantitatively assessed perceived message effectiveness (PME) of and reactance to candidate messages applying standard and comparative risk message framing, compared to adapted regulatory warnings used by the US Food and Drug Administration. Results: Qualitatively, interview and focus group participants found messages featuring novel information, including toxic constituents and physical harms (eg, hypertension), most effective. "Known" harms (eg, cancer) were described as effective by LGBTQ+ young adults due to the "shock value" of fear appeals. However, participants recommended pairing "known" harms with novel information; for example, addiction messaging was best received when described in the context of social or occasional use. Comparative messaging was appealing for harm reduction (ie, encouraging young adults who use cigarettes to quit smoking and use nicotine vapes), especially among LGBTQ+ participants. However, participants were concerned that comparative messages could unintentionally promote vaping among nicotine-naïve young adults. Qualitative participants preferred gain-framed efficacy messages that encouraged rather than demanded behavior change. Efficacy messages that emphasized "switching" were described as permissive for vaping, and participants were concerned that these may encourage sustained nicotine use. Some questioned whether vaping could effectively help young adults quit smoking. Survey results supported qualitative findings: messages with highest PME scores addressed toxic constituents, heart and lung disease, and cancer. Addiction messages were least effective. Among participants engaged in dual use, PME-smoking scores were higher when viewing candidate comparative messages than regulatory messages, but there were no significant differences between standard and comparative messages. The most effective candidate efficacy messages addressed quitting all smoking and vaping to reduce health risks. Messages that encouraged quitting smoking and switching to vapes were rated least effective. Conclusions: Comparative messaging was associated with higher PME-smoking among young adults engaged in dual use but did not consistently outperform standard messaging. Qualitative findings suggest that comparative framing may be misinterpreted as endorsing vaping as "safe" rather than "lower harm than cigarettes." Further research is needed to examine potential unintended consequences of comparative messaging, including sustained nicotine use among young adults who smoke or normalization of vaping among nicotine-naïve young adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".