Alcohol and cannabis use during the COVID-19 pandemic among transgender, gender-diverse, and cisgender adults in Canada
Bibliographic record
Abstract
BACKGROUND: This study examined whether heavy episodic drinking (HED), cannabis use, and subjective changes in alcohol and cannabis use during the COVID-19 pandemic differ between transgender and gender-diverse (TGD) and cisgender adults. METHODS: Successive waves of web-based cross-sectional surveys. SETTING: Canada, May 2020 to March 2021. PARTICIPANTS: 6,016 adults (39 TGD, 2,980 cisgender men, 2,984 cisgender women, and 13 preferred not to answer), aged ≥18 years. MEASUREMENTS: Measures included self-reported HED (≥5 drinks on one or more occasions in the previous week for TGD and cisgender men and ≥4 for cisgender women) and any cannabis use in the previous week. Subjective changes in alcohol and cannabis use in the past week compared to before the pandemic were measured on a five-point Likert scale (1: much less to 5: much more). Binary and ordinal logistic regressions quantified differences between TGD and cisgender participants in alcohol and cannabis use, controlling for age, ethnoracial background, marital status, education, geographic location, and living arrangement. RESULTS: Compared to cisgender participants, TGD participants were more likely to use cannabis (adjusted odds ratio (aOR)=3.78, 95%CI: 1.89, 7.53) and to have reported subjective increases in alcohol (adjusted proportional odds ratios (aPOR)= 2.00, 95%CI: 1.01, 3.95) and cannabis use (aPOR=4.56, 95%CI: 2.13, 9.78) relative to before the pandemic. Compared to cisgender women, TGD participants were more likely to use cannabis (aOR=4.43, 95%CI: 2.21, 8.87) and increase their consumption of alcohol (aPOR=2.05, 95%CI: 1.03, 4.05) and cannabis (aPOR=4.71, 95%CI: 2.18, 10.13). Compared to cisgender men, TGD participants were more likely to use cannabis (aOR=3.20, 95%CI: 1.60, 6.41) and increase their use of cannabis (aPOR=4.40, 95%CI: 2.04, 9.49). There were no significant differences in HED between TGD and cisgender participants and in subjective change in alcohol between TGD and cisgender men; however, the odds ratios were greater than one as expected. CONCLUSIONS: Increased alcohol and cannabis use among TGD populations compared to before the pandemic may lead to increased health disparities. Accordingly, programs targeting the specific needs of TGD individuals should be prioritized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".