Socio-demographic, health and drug use characteristics among people with different frequency patterns of cannabis use in Brazil
Bibliographic record
Abstract
Data on cannabis use patterns and health outcome indicators for Brazil are limited. We undertook a cross-sectional internet-based survey assessing socio-demographics, cannabis use frequency, other drug use, quality-of-life (QOL), affective states (i.e. anxiety and depression), and cannabis use disorder among adult respondents in Brazil. A multinomial logistic regression was conducted using cannabis frequency status as the dependent variable. A total of 6876 responders were identified as people with occasional (n = 1088), regular (n = 1062), and frequent (n = 4726) cannabis use. Groups differed greatly on sociodemographic characteristics and other drug use rates. Cannabis use disorder (p < 0.001), early-onset of cannabis use (aOR:1.71), and other substance use (aOR: from 1.53 to 2.59) were associated with a higher frequency of cannabis use. We did not find major differences in psychosocial and mental health outcomes as related to the frequency of cannabis use. Our results suggest that early initiation of cannabis, high use frequency, and using cannabis with other substances may elevate the risk for cannabis use disorder. Given the generally liberalizing trends for cannabis use, it is timely and appropriate to further study cannabis use and related outcomes in Brazil, considering that available indicators for this country are limited.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".