Cannabis use and psychotic‐like experiences trajectories during early adolescence: the coevolution and potential mediators
Bibliographic record
Abstract
BACKGROUND: The authors sought to model the different trajectories of psychotic-like experiences (PLE) during adolescence and to examine whether the longitudinal relationship between cannabis use and PLE is mediated by changes in cognitive development and/or change in anxiety or depression symptoms. METHODS: A total of 2,566 youths were assessed every year for 4-years (from 13- to 16-years of age) on clinical, substance use and cognitive development outcomes. Latent class growth models identified three trajectories of PLE: low decreasing (83.9%), high decreasing (7.9%), and moderate increasing class (8.2%). We conducted logistic regressions to investigate whether baseline levels and growth in cannabis use were associated with PLE trajectory membership. Then, we examined the effects of potential mediators (growth in cognition and anxiety/depression) on the relationship between growth in cannabis use and PLE trajectory. RESULTS: A steeper growth in cannabis use from 13- to 16-years was associated with a higher likelihood of being assigned to the moderate increasing trajectory of PLE [odds ratio, 2.59; 95% confidence interval (CI), 1.11-6.03], when controlling for cumulative cigarette use. Growth in depression symptoms, not anxiety or change in cognitive functioning, mediated the relationship between growth in cannabis use and the PLE moderate increasing group (indirect effect: 0.07; 95% CI, 0.03-0.11). CONCLUSIONS: Depression symptoms partially mediated the longitudinal link between cannabis use and PLE in adolescents, suggesting that there may be a preventative effect to be gained from targeting depression symptoms, in addition to attempting to prevent cannabis use in youth presenting increasing psychotic experiences.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".