Fact or Faction Regarding the Relationship between Cannabis Use and Violent Behavior.
Bibliographic record
Abstract
The relationship between cannabis use and violence, and to what extent this association is causal in nature, remains unclear. The aim of this scoping review was to ascertain whether cannabis use increases the risk of violence and aggression in adults. Because cannabis use can result in irritability, disinhibition, and altered cognition, it is plausible that its use increases the risk of violence and aggression and that this association is exacerbated in psychiatric illness. A search of the literature using PubMed, Scopus, and PsycINFO databases was performed; all materials published in English until April 2020 were considered. Peer-reviewed publications that assessed cannabis use and perpetration of violence or aggression in adults were included in this review. Of the 327 articles that were screened for eligibility, 19 articles met inclusion criteria for this review. Results suggest that there is a link between cannabis use and violence; however, this relationship is strictly correlational, and the strength of this relationship varies depending on the population (e.g., populations with severe and persistent mental illness versus the general population). These findings have important ramifications for treatment considerations and for public health and safety approaches.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".