Understanding the Relationships between ADHD Symptoms and Cannabis-Related Consequences among Young Adults
Bibliographic record
Abstract
Objective: The link between attention-deficit/hyperactivity disorder (ADHD) and cannabis-related problems is well documented, though research has primarily focused on cannabis use disorder (CUD) or cannabis consequences in aggregate. This study examined how inattentive (IN) versus hyperactive/impulsive (HI) ADHD symptoms relate to CUD symptoms as well as distinct domains of cannabis consequences (social-interpersonal consequences, impaired control, negative self-perception, self-care, risk behaviors, academic/occupational consequences, physical dependence, and blackout use) in young adults. Total amount of cannabis flower used over the past 90 days was explored as a potential mediator of these associations. Method: = 2.06) with a history of regular cannabis use completed self-report measures of ADHD symptoms and cannabis consequences. Participants also completed a 90-day Timeline Follow Back assessing grams of cannabis flower consumed each day, along with a structured clinical interview for CUD. Results: IN symptoms were directly associated with cannabis-related occupational/academic consequences, self-care consequences, and blackouts/memory impairment, independent of quantity of cannabis consumption. HI symptoms showed positive indirect associations with physical dependence, impaired control, and CUD through greater amount of cannabis used. Conversely, IN symptoms had negative indirect associations with these outcomes, mediated by amount of cannabis used. Conclusions: Findings reveal distinct pathways through which IN and HI ADHD symptoms relate to cannabis problems in young adults. Findings highlight the need to consider ADHD symptom domains separately when assessing specific cannabis-related risks, which may have implications for tailoring interventions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".