Latino Adults’ Perspectives on Treating Tobacco Use Via Social Media
Bibliographic record
Abstract
BACKGROUND: Latinos are the largest minority group in the United States, and in California they outnumber non-Hispanic whites. Smoking cessation programs tailored for Latino culture, and this population's specific smoking patterns, are needed. Online social networks for smoking cessation have high potential for Latinos, but have not been tested to date. OBJECTIVE: Building a research program on social media apps for cancer prevention in diverse populations, this qualitative study assessed acceptability of tobacco treatment that was distributed via social media for Latino smokers. METHODS: We conducted three focus groups with Latino adults who were former and current smokers recruited from Santa Clara County, California in 2015 (N=32). We assessed participants' smoking histories, attempts to quit, social media exposure, and receptivity to a social media-based smoking cessation intervention. Audio transcripts were translated and coded for themes. RESULTS: Participants reported factors driving their tobacco use and motivations to quit, and emphasized the importance of community and family in influencing their smoking initiation, cravings and triggers, attempts to quit, and abstinence. Participants valued the communal aspect of social media and suggested strategically tailoring groups based on key features (eg, age, gender, language preference). Participants reported preferring visual, educational, and motivational messages that were connected with existing services. CONCLUSIONS: Participants generally voiced acceptability of a social media-delivered intervention to help them quit smoking, viewed the intervention as well-equipped for catering to the strong community orientation of Latinos, and suggested that the platform was able to address variation within the population through strategic group creation. As a group member reflected, "Podemos hacerlo juntos" (We can do it together).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".