Down and High: Reflections Regarding Depression and Cannabis
Bibliographic record
Abstract
In light of the recent changes in the legal status of cannabis in Canada, the understanding of the potential impact of the use of cannabis by individuals suffering from depression is increasingly considered as being important. It is fundamental that we look into the existing literature to examine the influence of cannabis on psychiatric conditions, including mood disorders. In this article, we will explore the relationship that exists between depression and cannabis. We will examine the impact of cannabis on the onset and course of depression, and its treatment. We have undertaken a wide-ranging review of the literature in order to address these questions. The evidence from longitudinal studies suggest that there is a bidirectional relationship between cannabis use and depression, such that cannabis use increases the risk for depression and vice-versa. This risk is possibly higher in heavy users having initiated their consumption in early adolescence. Clinical evidence also suggests that cannabis use is associated with a worse prognosis in individuals with major depressive disorder. The link with suicide remains controversial. Moreover, there is insufficient data to determine the impact of cannabis use on cognition in individuals with major depression disorder. Preliminary evidence suggesting that the endogenous cannabinoid system is involved in the pathophysiology of depression. This will need to be confirmed in future positron emission tomography studies. Randomized controlled trials are needed to investigate the potential efficacy of motivational interviewing and/or cognitive behavioral therapy for the treatment of cannabis use disorder in individuals with major depressive major disorder. Finally, although there is preclinical evidence suggesting that cannabidiol has antidepressant properties, randomized controlled trials will need to properly investigate this possibility in humans.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 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".