Assessing Sex, Gender Identity, Sexual Orientation, Race, Ethnicity, Socioeconomic Status, and Mental Health Concerns in Tobacco Use Disorder Treatment Research: Measurement Challenges and Recommendations From a Society for Research on Nicotine and Tobacco Pre-conference Workshop
Bibliographic record
Abstract
This paper reports on topics discussed at a Society for Research on Nicotine and Tobacco pre-conference workshop at the 2019 annual Society for Research on Nicotine and Tobacco meeting. The goal of the pre-conference workshop was to help develop a shared understanding of the importance of several tobacco-related priority groups in tobacco use disorder (TUD) treatment research and to highlight challenges in measurement related to these groups. The workshop focused on persons with minoritized sex, gender identity, and sexual orientation identities; persons with minoritized racial and ethnic backgrounds; persons with lower socioeconomic status (SES); and persons with mental health concerns. In addition to experiencing commercial tobacco-related health disparities, these groups are also underrepresented in tobacco research, including TUD treatment studies. Importantly, there is wide variation in how and whether researchers are identifying variation within these priority groups. Best practices for measuring and reporting sex, gender identity, sexual orientation, race, ethnicity, SES, and mental health concerns in TUD treatment research are needed. This paper provides information about measurement challenges when including these groups in TUD treatment research and specific recommendations about how to measure these groups and assess potential disparities in outcomes. The goal of this paper is to encourage TUD treatment researchers to use measurement best practices in these priority groups in an effort to conduct meaningful and equity-promoting research. Increasing the inclusion and visibility of these groups in TUD treatment research will help to move the field forward in decreasing tobacco-related health disparities. Implications: Tobacco-related disparities exist for a number of priority groups including, among others, women, individuals with minoritized sexual and gender identities, individuals with minoritized racial and ethnic backgrounds, individuals with lower SES, and individuals with mental health concerns. Research on TUD treatments for many of these subgroups is lacking. Accurate assessment and consideration of these subgroups will provide needed information about efficacious and effective TUD treatments, about potential mediators and moderators, and for accurately describing study samples, all critical elements for reducing tobacco-related disparities, and improving diversity, equity, and inclusion in TUD treatment research.
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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 | 0.649 | 0.651 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.007 | 0.035 |
| Research integrity | 0.006 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".