Association between Non-Medical Cannabis Use and Anxiety Disorders in Women
Bibliographic record
Abstract
Introduction: Non-medical cannabis use and anxiety disorders are highly prevalent among Canadian women; however, the direction of this assocation remains controversial. The objective of this article is to provide an evidencebased update regarding the effect of non-medical cannabis on anxiety symptoms in women. Methods: A literature search was conducted using PsychINFO and MEDLINE for articles related to cannabis and marijuana use among women with anxiety or anxiety disorders. Only English language literature from 2010 to 2020 was reviewed. Studies including patients under the age of 18 and studies addressing medical cannabis were excluded. Four studies met our inclusion criteria for this review. Results: Cannabis use and anxiety disorders are both highly prevalent among young women. Other substance use in addition to cannabis is frequently reported by women. Reasons for cannabis use by women with anxiety differed from those of men. Findings did not show a direct association between cannabis use and anxiety symptoms. Women who used cannabis did not report higher rates of anxiety nor did anxiety predict the onset of cannabis use. Conclusion: There is no evidence to indicate that non-medical cannabis use worsens anxiety symptoms among women. Further studies should focus on reducing potential confounding factors and developing a reliable method of quantifying cannabis use in order to determine the direction of the interaction between cannabis and anxiety disorders among women.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".