Chinese immigrant smokers’ access barriers to tobacco cessation services and experience using social media and text messaging
Bibliographic record
Abstract
INTRODUCTION: Smoking rates remain disproportionately high among Chinese immigrants in the US, particularly in males. Community-based smoking cessation services and quitlines have low engagement rates. Social media and text messaging programs can be effective in promoting quit rates and improving treatment engagement. This study examined Chinese immigrant smokers' barriers to accessing available smoking cessation services and patterns of using social media platforms and mobile phone text messaging. METHODS: We conducted in-depth interviews (n=30) and a brief survey (n=49) with adult Chinese immigrant smokers leaving in New York City in 2018. Qualitative interviews explored smokers' challenges with smoking cessation, barriers to accessing and using smoking cessation services, and experience using social media and text messaging. The quantitative survey assessed smoking and quitting behaviors, and social media and text messaging use patterns. RESULTS: Qualitative data revealed that participants faced various barriers to accessing cessation services, including the lack of awareness about services, skepticism about treatment effects, reliance on willpower for cessation, and time constraints. WeChat was mainly used to maintain social networking and acquire information. Participants rarely used text messaging or other social media platforms. Quantitative data showed that 55% of participants had no plan to quit smoking. Among those who reported past-year quit attempts (45%), 55% used cessation assistance. WeChat was the most frequently used platform with 94% users. CONCLUSIONS: WeChat has potential to serve as an easily accessible platform for delivering smoking cessation treatment among Chinese immigrant populations. Research is warranted to explore the feasibility and efficacy of employing WeChat in smoking cessation treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".