Tobacco and marijuana use during the COVID-19 pandemic lockdown among American Indians residing in California and Oklahoma
Bibliographic record
Abstract
INTRODUCTION: American Indian (AI) people experience a disproportionate tobacco and marijuana burden which may have been exacerbated by the COVID-19 pandemic. Little is known about the tobacco and marijuana habits of American Indian individuals during the COVID-19 pandemic. The objective of this study is to examine tobacco and marijuana use as well as change in use during the COVID-19 pandemic among the American Indian community. METHODS: This cross-sectional study analyzes survey data from a convenience sample of American Indian individuals residing in California and Oklahoma and included adults with and without cancer that resided in both rural and urban areas (n=1068). RESULTS: During October 2020 - January 2021, 36.0% of participants reported current use of tobacco products, 9.9% reported current use of marijuana products, and 23.7% reported increased use of tobacco and/or marijuana in the past 30 days, with no difference between those with cancer and those without cancer. Tobacco use was associated with marital status, age, employment status, COVID-19 exposure, COVID-19 beliefs, and alcohol consumption. Marijuana use was associated with COVID-19 beliefs, alcohol consumption, and income level. Increased tobacco and/or marijuana use was associated with baseline use of those products. Nearly a quarter of participants reported increased use of tobacco and/or marijuana products during the COVID-19 pandemic. CONCLUSIONS: We observed high rates of tobacco use during the COVID-19 pandemic, consistent with other studies. Research is needed to examine whether tobacco and marijuana use will decrease to pre-pandemic levels post-pandemic or if these behaviors will persist post-pandemic. Given these findings, there is a pressing need to increase access to evidence-based tobacco and marijuana treatment services in the AI population post COVID-19 pandemic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".