Concurrent validity of the marijuana purchase task: a meta‐analysis of trait‐level cannabis demand and cannabis involvement
Bibliographic record
Abstract
Abstract Background and aims The Marijuana Purchase Task (MPT) is increasingly used to measure cannabis reinforcing value and has potential use for cannabis etiological and regulatory research. This meta‐analysis sought to evaluate for the first time the MPT's concurrent validity in relation to cannabis involvement. Methods Electronic databases and pre‐print repositories were searched for MPT studies that examined the cross‐sectional relationship between frequency and quantity of cannabis use, problems, dependence, and five MPT indicators: intensity (i.e. unrestricted consumption), Omax (i.e. maximum consumption), Pmax (i.e. price at which demand becomes elastic), breakpoint (i.e. first price at which consumption ceases), and elasticity (i.e. sensitivity to rising costs). Random effects meta‐analyses of cross‐sectional effect sizes were conducted, with Q tests for examining differences by cannabis variables, meta‐regression to test quantitative moderators, and publication bias assessment. Moderators included sex, number of MPT prices, variable transformations, and year of publication. Populations included community and clinical samples. Results The searches yielded 14 studies (n = 4077, median % females: 44.8%: weighted average age = 29.08 [SD = 6.82]), published between 2015 and 2022. Intensity, Omax, and elasticity showed the most robust concurrent validity (|r's| = 0.147–325, ps < 0.014) with the largest significant effect sizes for quantity (|r| intensity = 0.325) and cannabis dependence (|r| Omax = 0.320, |r| intensity = 0.305, |r| elasticity = 0.303). Higher proportion of males was associated with increased estimates for elasticity‐quantity and Pmax‐problems. Higher number of MPT prices significantly altered magnitude of effects sizes for Pmax and problems, suggesting biased estimations if excessively low prices are considered. Methodological quality was generally good, and minimal evidence of publication bias was observed. Conclusions The marijuana purchase task presents adequate concurrent validity to measure cannabis demand, most robustly for intensity, Omax, and elasticity. Moderating effects by sex suggest potentially meaningful sex differences in the reinforcing value of cannabis.
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 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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".