A Scoping Review of Trends in the Size of Lesbian, Gay, and Bisexual Tobacco Use Disparities, 1996–2020, United States and Canada
Bibliographic record
Abstract
Purpose:Tobacco use is a major health disparity for lesbian, gay, bisexual, and transgender (LGBT) populations compared with heterosexual/cisgender populations. In this scoping review, we aimed to determine if LGBT tobacco use disparities are improving or worsening over time and if trends in disparities differed across subgroups. Methods:We included articles that longitudinally explored youth and adult LGB tobacco use in the United States and Canada after searching four databases and capturing records through July 2022. Two reviewers independently screened the title/abstract and full text of 2326 and 45 articles, respectively. Eleven articles from 18 larger assessments met inclusion criteria, spanning data collection from 1996 to 2020. Results:All studies consistently demonstrated tobacco disparities for LGB populations. No articles examined longitudinal transgender tobacco disparities. Most studies focused on smoking combustible cigarettes. Disparities in heavy or daily use for all LGB youth subgroups compared with heterosexual samples appear to be shrinking longitudinally. Results for early-onset, current, and lifetime smoking were less consistent. Adult evidence was relatively sparse; however, after 2010, studies show diminishing disparities over time, except for current smoking by bisexual women. Conclusions:Large tobacco use disparities persist for LGB populations, although the size of disparities may be decreasing for some groups. Initiatives for lesbian and bisexual women and girls should be prioritized, in addition to interventions addressing LGB smoking broadly. Surveillance instruments should uniformly and consistently assess LGBT identities and tobacco use behaviors.
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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 | 0.022 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.050 | 0.071 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".