Development of Brief Alcohol and Cannabis Motives Measures: Psychometric Evaluation Using Expert Feedback and Longitudinal Methods
Bibliographic record
Abstract
Objective: Alcohol and cannabis use motives are often studied as contributors to risky substance use patterns. While various measures for capturing such motives exist, most contain 20+ items, which render their inclusion in certain research designs (e.g., daily diary) or with certain populations (e.g., polysubstance users) unfeasible. We sought to generate and validate six-item measures of cannabis and alcohol motives from existing measures, the Marijuana Motives Measure (MMM) and the Modified Drinking Motives Questionnaire-Revised (MDMQ-R). Methods: In Study 1, items were generated, feedback from 33 content-domain experts was obtained, and item revisions were made. In Study 2, the finalized brief cannabis and alcohol motives measures, along with the MMM, MDMQ-R, and substance-related measures, were administered to 176 emerging adult cannabis and alcohol users (71.6% female) at two timepoints, two months apart. Participants were recruited through a participant pool. Results: Study 1 experts indicated satisfactory ratings of face and content validity. Expert feedback was used to revise three items. Study 2 results suggest test-retest reliabilities for the single-item forms (r = .34 to .60) were similar to those obtained with full motives measures (r = .39 to .67). Validity was acceptable-to-excellent in that brief and full-length measures were significantly intercorrelated (r = .40 to .83). The brief and full-length measures had similar concurrent and predictive relationships for cannabis and alcohol quantity x frequency (coping-with-anxiety for cannabis and enhancement for alcohol) and problems (coping-with-depression), respectively. Conclusions: The brief measures represent psychometrically-sound measures of cannabis and alcohol use motives with substantially less participant burden than the MMM and MDMQ-R.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".