The Association between Recent Cannabis Use and Suicidal Ideation in Adults: A Population-based Analysis of the NHANES from 2005 to 2018
Bibliographic record
Abstract
Objective: With the increasing prevalence of cannabis use, there is a growing concern about its association with depression and suicidality. The aim of this study was to examine the relationship between recent cannabis use and suicidal ideation using a nationally representative data set. Methods: A cross-sectional analysis of adults was undertaken using National Health and Nutrition Examination Survey data from 2005 to 2018. Participants were dichotomized by whether or not they had used cannabis in the past 30 days. The primary outcome was suicidal ideation, and secondary outcomes were depression and having recently seen a mental health professional. Multiple logistic regression was used to adjust for potential confounders, and survey sample weights were considered in the model. Results: Compared to those with no recent use ( n = 18,599), recent users ( n = 3,127) were more likely to have experienced suicidal ideation in the past 2 weeks (adjusted odds ratio [aOR] 1.54, 95% CI, 1.19 to 2.00, P = 0.001), be depressed (aOR 1.53, 95% CI, 1.29 to 1.82, P < 0.001), and to have seen a mental health professional in the past 12 months (aOR 1.28, 95% CI, 1.04 to 1.59, P = 0.023). Conclusions: Cannabis use in the past 30 days was associated with suicidal thinking and depression in adults. This relationship is likely multifactorial but highlights the need for specific guidelines and policies for the prescription of medical cannabis for psychiatric therapy. Future research should continue to characterize the health effects of cannabis use in the general population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".