Smoking Cessation Among Gender Minority Populations, Cis-women, and Cis-men: Findings From the International Tobacco Control Netherlands Survey
Bibliographic record
Abstract
INTRODUCTION: Little is known about smoking cessation among gender minority populations compared to cisgender individuals (whose gender matches their sex assigned at birth). We examined differences between smokers from gender minority populations, cis-women, and cis-men in the heaviness of smoking, quit intentions, use of cessation assistance, quit attempts (ever tried and number), and triggers for thinking about quitting. AIMS AND METHODS: We used cross-sectional data from the 2020 International Tobacco Control Netherlands Survey. Among smoking respondents, we distinguished (1) cis-women (female sex, identified as women, and having feminine gender roles; n = 670), (2) cis-men (male sex, identified as men, and having masculine gender roles; n = 897), and (3) gender minorities (individuals who were intersex, who identified as nonbinary, genderqueer, had a sex/gender identity not listed, whose gender roles were not feminine or masculine, or whose gender identity and/or roles were not congruent with sex assigned at birth; n = 220). RESULTS: Although gender minorities did not differ from cis-women and cis-men in the heaviness of smoking, plans to quit smoking, and quit attempts, they were significantly more likely to use cessation assistance (20% in the past 6 months) than cis-women (12%) and cis-men (9%). Gender minorities were also significantly more likely to report several triggers for thinking about quitting smoking, for example, quit advice from a doctor, an anti-smoking message/campaign, and the availability of a telephone helpline. CONCLUSION: Despite equal levels of quit attempts and heaviness of smoking, gender minority smokers make more use of smoking assistance, and respond stronger to triggers for thinking about quitting smoking. IMPLICATIONS: Smoking cessation counselors should be sensitive to the stressors that individuals from any minority population face, such as stigmatization, discrimination, and loneliness, and should educate their smoking clients on effective coping mechanisms to prevent relapse into smoking after they experience these stressors. Developing tailored smoking cessation programs or campaigns specifically for gender minority populations can also be useful. Based on the results of our subgroup analyses, programs or campaigns for younger gender minority smokers could focus on the availability of telephone helplines and on how friends and family think about their smoking behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".