Cannabis use, risk behaviours and harms in Brazil: A comprehensive review of available data indicators
Bibliographic record
Abstract
ISSUES: Cannabis use and related health/social outcome indicator data for Brazil-where non-medical cannabis is generally illegal-are limited. APPROACH: Towards a comprehensive overview of relevant indicators, we searched primary databases by combining MeSH-index terms related to cannabis, geographic location and subtopic terms (e.g., use, health, mortality) focusing on cannabis use and key outcome indicators in Brazil since 2010. In addition, relevant 'grey literature' (e.g., survey reports) was identified. Key indicator data were mainly narratively summarised. KEY FINDINGS: Overall, cannabis use has increased somewhat since pre-2010, with (past-year) use rates measured at 2-3% for general population adults, yet 5% or higher among youth and/or (e.g., post-secondary) student populations. For key risk behaviours, the presence of tetrahydrocannabinol-positivity among motor-vehicle drivers has been measured at <2%. While the prevalence of cannabis use disorder appears to have decreased, the relative proportion of treatment provided for cannabis-related problems increased. National- and local-based studies indicated an association of cannabis use with mental health harms, including depression and suicidality. Although some non-representative and/or local studies contain information, other monitoring data, including cannabis-related risks and harms (e.g., cannabis-related driving, mortality, hospitalisations), are limited in availability. IMPLICATIONS AND CONCLUSION: The prevalence of cannabis use in Brazil is comparably low (e.g., relative to elsewhere in the Americas). Data on numerous key cannabis-related indicators is absent, or limited in scope for Brazil. Considering ongoing evolutions in cannabis control and its status as the most common illicit drug, more comprehensive surveillance of cannabis use and related outcomes is advised.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.035 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".