Sleep as a Mediator Between Cannabis Use and Psychosis Vulnerability: A Longitudinal Cohort Study
Bibliographic record
Abstract
Abstract Objectives Increasing evidence implicates cannabis consumption as a key risk factor in the development of psychosis, but the mechanisms underpinning this relationship remain understudied. This study proposes to determine whether sleep disruption acts as a mediator of the cannabis-to-psychosis relationship. Study Design This longitudinal study assessed measures of cannabis use frequency, sleep quality (SQ), and psychotic-like experiences (PLEs) were collected using self-reported questionnaires. Data were collected from September 2012 to September 2018. Data were collected from a general population sample of adolescents who entered the seventh grade in 31 schools in the Greater Montreal area. The study uses data collected on an annual basis from 3801 high school students from grades 7 to 11. The aforementioned measures were measured using the Detection of Alcohol and Drug Problems in Adolescents questionnaire, a SQ Likert scale, and measures the Psychotic-Like Experiences Questionnaire for Children. Study Results Results show a reciprocal 1-year cross-lagged effect of cannabis use and sleep (β = −0.076, 95% CI = −0.037 to −0.018, P = .000), of sleep on cannabis use (β = −.016, 95% CI = −0.025 to −0.006, P = .007), of sleep on PLEs (β = −0.077, 95%CI = −0.014 to −0.051, P = .000), and of PLEs on sleep (β = −0.027, 95% CI = −0.037 to −0.018, P = .000). We additionally found a 2 years indirect lagged-effect of cannabis use on PLEs (β = 0.068, 95% CI = 0.024 to 0.113, P = .011) mediated by 1-year sleep (β = 0.006, 95% CI = 0.003 to 0.009, P = .001). Conclusions Our results suggest sleep disruptions simultaneously aggravate, and are aggravated by, cannabis addiction and PLEs. The longitudinal sleep-mediated effect of cannabis use on PLEs encourages further research into the role of sleep as a potential therapeutic target in the prevention of cannabis-related psychosis.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".