Clouded Minds: A Systematic Review on the Link Between Cannabis Use and Depression in Young Adults
Bibliographic record
Abstract
Introduction: Cannabis use is most prevalent among young adults, a time of critical brain development during which cannabis may disrupt neurological processes. Such disruptions may lead to cognitive impairments or increase susceptibility to mental health conditions, including depression. While prior research has identified a correlation between cannabis use and depression, this relationship may also stem from self-medication. Despite the developmental significance of this life stage, no comprehensive review has been written that focuses on the association between cannabis use and depression solely in young adults. The present review aims to address this gap by investigating whether cannabis use is linked to depression in this population. Methods: Studies that examined the association between cannabis use and depression among young adults using a cross-sectional or cohort design were selected. To find these articles, literature searches were performed using the databases: PsychINFO, ProQuest Theses and Dissertations Global, MEDLINE, Pubmed, and Academic Search Complete. A total of nine articles satisfying the inclusion/exclusion criteria were found. Results: There was a consistent positive relationship observed between cannabis use and depression. Out of the 11 measures examined, only one failed to report a positive association. However, many of these relationships were relatively weak, with only six of the eleven demonstrating both a positive and statistically significant connection. Discussion: We observed a weak but consistent relationship between cannabis use and depression, indicating a subtle yet consistent correlation. This suggests that young adults who use cannabis tend to exhibit a higher prevalence of depression compared to their non-using same age counterparts. Conclusion: Young adults who use cannabis appear to have higher rates of depression compared to their non-using peers. However, the cross-sectional nature of the selected studies limits findings to correlation rather than causation. Limitations also arise from the present study not fully meeting the criteria for a systematic review, such as the absence of a risk of bias assessment. Despite these limitations, further research is urgently needed due to the significant public health implications of this topic.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".