Behavioral economic analysis of legal and illegal cannabis demand in Spanish young adults with hazardous and non-hazardous cannabis use
Bibliographic record
Abstract
In October 2021, a legal framework that regulates cannabis for recreational purposes in Spain was proposed, but research on its potential impacts on cannabis use is currently limited. This study examined the reliability and discriminant validity of two Marijuana Purchase Tasks (MPTs) for measuring hypothetical legal and illegal cannabis demand, and to examine differences in demand of both commodities in young adults at hazardous vs. non-hazardous cannabis use risk levels. A total of 171 Spanish young adults [Mage= 19.82 (SD=1.81)] with past-month cannabis use participated in a cross-sectional study from September to November 2021. Two 27-item MPTs were used to estimate hypothetical demand for legal and illegal cannabis independently. The Cannabis Use Disorder Identification Test (CUDIT-R) was used to assess hazardous cannabis use and test for discriminant validity of the MPTs. Reliability analyses were conducted using Classical Test Theory (Cronbach’s alpha) and Item Response Theory (Item Information Functions). The MPT was reliable for measuring legal (α=.94) and illegal (α=.90) cannabis demand. Breakpoint (price at which demand ceases), and Pmax (price associated with maximum expenditure) were the most sensitive indicators to discriminate participants with different levels of the cannabis reinforcing trait. No significant differences between legal and illegal cannabis demand in the whole sample were observed, but hazardous vs. non-hazardous users showed higher legal and illegal demand, and decreased Breakpoint and Pmax if cannabis were legal vs illegal. The MPT exhibits robust psychometric validity and may be useful to inform on cannabis regulatory science in Spain.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".