Cannabis Use as a Risk Factor for Depression, Anxiety, and Suicidality
Bibliographic record
Abstract
Psychiatric disorders and substance use disorders commonly co-occur and are major public health concerns given the morbidity and mortality associated with them. Globally, cannabis is among the most commonly used drugs, and cannabis use frequently begins in adolescence or emerging adulthood, both important periods of development and periods in which psychiatric symptoms and disorders frequently emerge. Thus, the relationships between cannabis use and mental illnesses are essential for nurses and other healthcare professionals and researchers to explore and understand. This literature review examines the relationships between cannabis use and depression, anxiety, and suicide. It includes a primer on the neurobiology of cannabis effects; an overview on the epidemiological evidence on the associations between cannabis use and depression, anxiety, and suicide; and a discussion of implications for nurses, particularly important given changes in the medical and recreational cannabis legislation in North America. Overall, this review found consistent evidence showing a cross-sectional association between recreational cannabis use and depression, anxiety, and suicide, despite some limitations and conflicts in the literature. In addition, most evidence from longitudinal or case control studies suggested cannabis use preceded the development of depressive symptoms and suicidal behaviors, although the quality of this evidence was mixed. Implications for future research and nursing practice are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".