Investigating predictors of problematic cannabis use in polysubstance users
Bibliographic record
Abstract
Introduction: Since its legalization in 2018, cannabis use has substantially increased in Canada. This increased use is concerning, as one in every eleven cannabis users will go on to develop a cannabis use disorder. Further, problematic cannabis use is often related to the use of additional substances, particularly nicotine and alcohol, and there is evidence to suggest that the degree of harms associated with cannabis use increases when cannabis is used in conjunction with other substances. Additionally, personality is a known risk factor for problematic substance use, although to date problematic cannabis use has not been consistently linked to any specific personality trait. This study aimed to investigate the relationship between substance use, personality, and problematic cannabis use in a sample of cannabis using polysubstance users. Method: A sample of 521 polysubstance users (past 30-day users of cannabis, alcohol, and nicotine) completed an online survey measuring their substance use, dependence, and personality. Levels of substance specific dependence was measured using the Cannabis Use Disorder Identification Test – Revised, the Alcohol Use Disorders Identification Test, and the Fagerström Tests for Cigarette and E-cigarette Dependence, while personality was measured using the Substance Use Risk Profile Scale (SURPS). Results: Regression analyses showed that the top predictors for problematic cannabis use levels were levels of alcohol dependence, cigarette/e-cigarette dependence, impulsivity, and sensation seeking. Further analyses compared those who met the criteria for problematic cannabis use to those who did not; problematic cannabis users had significantly higher levels of alcohol and nicotine dependence, as well as higher levels of impulsivity and sensation seeking (all p’s <.001). Discussion: This study identified strong relationships of problematic cannabis use with problematic alcohol and cigarette/e-cigarette use, and with sensation seeking and impulsivity. The findings have implications for screening, intervention, and policy. For example, the strong relations of problematic cannabis use with problematic alcohol use speak to the inadvisability of the co-location of cannabis and alcohol sales, as is the case in several jurisdictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".