Blowing Minds: A cross-cultural, longitudinal investigation to unravel the highs and lows of recreational and medicinal cannabis users
Bibliographic record
Abstract
Cannabis use is widely on the rise for recreational and medicinal purposes, with the latter often aimed at improving mental health and cognition. Simultaneously, the prevalence of cannabis-related health harms, such as cannabis use disorder (CUD), is increasing. Product potency, route of administration, and preexisting mental health symptoms play a role in the effects of cannabis; however, there is limited research on how broader factors, such as local cannabis legislation and perceptions of use, may influence these outcomes. Moreover, knowledge remains sparse regarding the growing population of medicinal users, predominantly using without medical supervision. Altogether, this illustrates the need for ecologically valid ways to investigate individuals and their cannabis usage within real-world settings. Therefore, this longitudinal, cross-cultural online study examines an international sample of regular recreational and medicinal users (aged 18-65) from different cannabis jurisdictions (e.g., the Netherlands, the United States, Brazil, Canada, and regions in Asia and Africa) in their natural environment via their mobile phone. By using the Experience Sampling Method (ESM), alongside a comprehensive battery of cognition and mental health related assessments over a 2-year period, we aim to gain insight into the short- and long-term mental health and cognitive determinants and consequences of cannabis use, and how these interact with broader, contextual factors, such as legislation. This is one of the first cross-cultural studies assessing both positive and negative effects of cannabis longitudinally, examining the full range of effects that cannabis may have on the individual.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".