Abstract 6192: Disparities in cancer-causing substance use in trans and gender diverse communities: Canadian population-based data
Bibliographic record
Abstract
This study utilized data from the Canadian Community Health Survey, which is an annual, population-based, cross-sectional survey that covers topics such as physical and mental health, chronic health conditions, and lifestyle behaviors. The 2019 and 2020 Canadian Community Health Survey was pooled, and a gender modality variable was derived by comparing sex assigned at birth and gender identity. The gender modality variable was classified into two categories: 1) Cisgender (CG) people who had a sex assigned at birth that aligned with their gender identity, and 2) Trans and gender diverse (TGD) people who had a sex assigned at birth that differed from their gender identity. Both complete case and imputed datasets were used in this analysis. For imputed datasets, multiple imputation with chained equations was used to handle missing data. Survey and bootstrap weights were applied to unadjusted and adjusted logistic regression analyses, where gender modality was the explanatory variable, and substance use types were the dependent variables. This study found that there were no significant differences in alcohol, smoking, and e-cigarettes use by gender modality in both adjusted and unadjusted models. However, TGD people had higher odds of daily or almost daily cannabis use, compared to CG people, while adjusting for covariates (adjusted odds ratio (aOR): 3.7, 95% confidence interval (CI): 1.7-7.8). Future research should determine the level of knowledge in TGD communities for cancer-related implications of cannabis use. In addition, the acceptance and feasibility of harm reduction programs tailored for TGD communities, should be explored. Citation Format: Ace Chan, Hannah Kia, Travis Salway, Trevor Dummer. Disparities in cancer-causing substance use in trans and gender diverse communities: Canadian population-based data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6192.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".