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 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 | 0.050 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".