Effects of cannabis dependence on sleep quality and cognitive function: A comparative study in moroccan adolescent addicts and non-addicts
Bibliographic record
Abstract
Cannabis use during adolescence is a growing public health concern, particularly due to its potential effects on brain development, cognitive function, and sleep quality. While the prevalence of cannabis use among Moroccan youth is high, scientific studies exploring its neuropsychological consequences in this population remain limited. This study aimed to examine and compare cognitive performance and sleep quality between cannabis-addicted and non-addicted Moroccan adolescents, using validated assessment tools. A cross-sectional comparative study was conducted among 200 adolescents aged 14 to 24, recruited from the Guéliz Addiction Center in Marrakech. Participants were classified into addicted and non-addicted groups based on The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA), and sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Statistical analyses included Mann-Whitney U tests, Spearman correlations, and Principal Component Analysis (PCA). Addicted adolescents showed significantly lower MoCA scores across several domains, including memory, attention, and language (p < 0.01). They also reported significantly poorer sleep quality, with higher scores in PSQI components such as sleep latency, nighttime disturbances, and use of sleep medications (p < 0.01). PCA revealed distinct latent dimensions associated with both cognitive and sleep impairments, with memory and daytime dysfunction emerging as major contributors. A moderate to strong correlation was found between addiction diagnosis, cognitive decline, and sleep disruption. Cannabis addiction in Moroccan adolescents is associated with significant impairments in cognition and sleep. These findings highlight the need for integrated assessment tools and culturally tailored interventions to address cannabis use and its consequences in this vulnerable 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".