Smoking-Related Stigma: A Public Health Tool or a Damaging Force?
Bibliographic record
Abstract
BACKGROUND: Tobacco control policies and other denormalization strategies may reduce tobacco use by stigmatizing smoking. This raises an important question: Does perceived smoking-related stigma contribute to a smoker's decision to quit? The aim of this study was to evaluate if perceived smoking-related stigma was associated with smoking cessation outcomes among smokers in Mexico and Uruguay. METHODS: We analyzed prospective data from a panel of adult smokers who participated in the 2008-2012 administrations of the International Tobacco Control Policy Evaluation Surveys in Mexico and Uruguay. We defined two analytic samples of participants: the quit behavior sample (n = 3896 Mexico; n = 1525 Uruguay) and the relapse sample (n = 596 Mexico). Generalized estimating equations were used to evaluate if different aspects of perceived stigma (ie, discomfort, marginalization, and negative stereotype) at baseline were associated with smoking cessation outcomes at follow-up. RESULTS: We found that perceived smoking-related stigma was associated with a higher likelihood of making a quit attempt in Uruguay but with a lower likelihood of successful quitting in Mexico. CONCLUSIONS: This study suggests that perceived smoking-related stigma may be associated with more quit attempts, but less successful quitting among smokers. It is possible that once stigma is internalized by smokers, it may function as a damaging force. Future studies should evaluate the influence of internalized stigma on smoking behavior. IMPLICATIONS: Although perceived smoking-related stigma may prompt smokers to quit smoking, smoking stigma may also serve as a damaging force for some individuals, making quitting more difficult. This study found that perceived smoking-related stigma was associated with a higher likelihood of making a quit attempt in Uruguay but with a lower likelihood of successful quitting in Mexico.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".