Chronic Cannabis Use in Everyday Life: Emotional, Motivational, and Self-Regulatory Effects of Frequently Getting High
Bibliographic record
Abstract
Approximately 200 million people consume cannabis annually, with a significant proportion of them using it chronically. Using experience sampling, we describe the effects of chronically getting high on emotions, motivation, effort, and self-regulation in everyday life. We queried chronic users ( N = 260) 5 times per day over 7 days (3,701 observations) to assess immediate effects of getting high and longer term, between-person effects. Getting high was associated with more positive emotions and fewer negative emotions. Contrary to stereotypes, we observed minimal effects on motivation or objective effort willingness. However, getting high was associated with lower scores on facets of conscientiousness. Surprisingly, there was no evidence of a weed hangover. Relative to less frequent users, very frequent users exhibited more negative emotions dispositionally, but they were more motivated. They also reported less self-control and willpower. As attitudes about cannabis are changing, our findings provide a rich description of its chronic use.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".