Measurement invariance of the Marijuana Motives Measure among men and women using Stop Cannabis App
Bibliographic record
Abstract
BACKGROUND: Motives to use cannabis play a central role in the development and maintenance of problematic cannabis use and previous studies stressed sex-related differences on motives to use cannabis. However, motives cannot be validly compared in men and women without first establishing the measurement invariance across sex. Therefore, the aim of the study is to (1) examine for the first time the measurement and structural invariance of the Marijuana Motives Measure (MMM) across sex, and (2) to investigate the motives for cannabis use that best explain problematic use. METHODS: 2951 (41.7% women) users of the "Stop cannabis" smartphone app of which 99.8% reported having used cannabis in the last three months completed an online MMM and ASSIST to assess the severity of their problematic cannabis use. RESULTS: Multigroup confirmatory factor analyses supported measurement invariance across sex, whereas structural invariance was not confirmed. Indeed, group comparisons indicated that women reported greater coping motives then men whereas men showed greater social motives than women. A multiple linear regression analysis showed that only coping and conformity motives were significantly associated with greater problematic cannabis use, whereas neither sex nor the sex by motives interactions were significantly related to problematic cannabis use. CONCLUSIONS: The MMM appears to function comparably across men and women. Therefore, sex-related comparisons on the questionnaire can be considered valid. Coping and conformity motives may play a central role part in the development of marijuana use problems which may hold implications for intervention development and public policy.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".