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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".