Developing Graphic Messages for Vaping Prevention Among Black and Latino Adolescents: Participatory Research Approach
Bibliographic record
Abstract
BACKGROUND: As an important transition stage in human development, adolescence is a critical window for vaping prevention. There is a substantial gap in communication research on vaping prevention among racial and ethnic minority groups. Their representation is essential to develop, implement, and disseminate innovative and effective interventions for vaping prevention. OBJECTIVE: The aim of this study is to describe the participatory research (PR) procedures used with Black and Latino adolescents to develop culturally and linguistically appropriate graphic messages for vaping prevention. METHODS: This PR study used a qualitative, user-centered design method. We conducted a series of focus groups with 16 Black and Latino adolescents to develop culturally and linguistically appropriate graphic messages for vaping prevention. The biobehavioral model of nicotine addiction provided a framework for the development of the graphic messages. Participants met 4 times to provide iterative feedback on the graphic messages until they reached a consensus on overall quality and content. RESULTS: At baseline, the participants' mean age was 15.4 years (SD 1.4). Of the participants, 50% (8/16) were female, 88% (14/16) were heterosexual, 56% (9/16) were Black/African American, and 44% (7/16) were Hispanic/Latino. A total of 12 of the 16 participants (75%) chose to participate in the English sessions. Participants decided to create four types of graphic messages: (1) financial reward, (2) health reward, (3) social norms, and (4) self-efficacy. Meeting 4 times with the 4 groups provided sufficient opportunities for iterative feedback on the graphic messages to reach a consensus on overall quality and content. CONCLUSIONS: It is feasible and practical to build PR among Black and Latino adolescents focused on vaping prevention. Adolescents added innovation and creativity to the development of culturally and linguistically appropriate graphic messages for vaping prevention. Appropriate staffing, funding, and approaches are key for successful PR efforts among Black and Latino adolescents. Future research is needed to evaluate the impact of the graphic messages on vaping prevention.
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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 | 0.058 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".