Psychological correlates of risky cannabis use: Alexithymia, frontal lobe dysfunction and impulsivity
Bibliographic record
Abstract
A community sample of young adult cannabis users was recruited. Of the 138 participants, 71.7% were defined by their Cannabis Use Disorder Identification Test (CUDIT) scores as low risk cannabis users, whereas 28.3% were risky cannabis users. The CUDIT risk group was significantly associated with the Toronto Alexithymia Scale (TAS-20) defined alexithymia group, p = .004. CUDIT scores were significantly positively correlated with all three TAS-20 alexithymia subscale scores, Barratt Impulsiveness Scale (BIS-11) impulsivity, and all three frontal lobe dysfunction subscales of the Frontal Systems Behavior Scale (FrSBe). A two-way (CUDIT risk group X gender) between-subjects multivariate analysis of covariance (MAN-COVA) was performed on scores obtained from the TAS-20 subscales, FrSBe subscales, and BIS-11; using age as the covariate. The multivariate effect of the CUDIT group was significant, p < .0001. Univariate effects of the CUDIT group were significant for all measures. Heavy cannabis use is known to be associated with residual effects including deficits in frontal lobe functioning that may persist for up to five weeks of abstinence; thus, it is tempting to regard these correlates of risky cannabis use as reflecting residual effects. However, traits such as alexithymia, poor impulse control and executive dysfunction have all been linked to the predisposition to abuse drugs and alcohol. Both directions of causation may apply, for example, those with inherently lower emotion regulation and executive self-control abilities may be more likely to abuse substances, and substance abuse itself may further impair executive functioning and self-regulation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".