When Chilling Out Casts Doubt: Exploring the Temporal Associations Among Anxiety, Depression, and Cannabis Use in Emerging Adulthood
Bibliographic record
Abstract
Research shows that both cannabis use and emotional problems (anxiety and depression) tend to peak in emerging adulthood. There is a relative paucity of research examining the temporal associations between cannabis use and emotional problems among emerging adults. Accordingly, this multi-wave longitudinal study examined three competing models of temporal precedence: the vulnerability model (negative emotions precede cannabis use); the scar model (cannabis use precedes negative emotions), and the reciprocal model (bidirectional associations between cannabis use and negative emotions). A sample of 299 North American emerging adults ( M age = 23.69 years, 58% female) completed three waves of survey measures and cross-lagged panel models were run to evaluate how anxiety and depression were related to both cannabis use and related problems across the 1-month study period. Regarding anxiety symptoms, some support was found for the vulnerability model, in that anxiety preceded cannabis problems across some waves. No directional or reciprocal associations between anxiety and cannabis use were found. As for depression symptoms, there was support for reciprocal links between cannabis problems and depression across waves. However, consistent with the anxiety-related findings, no directional or reciprocal associations between depression and cannabis use were found. The scientific and practical implications of these findings 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".