Measuring recent cannabis use across modes of delivery: Development and validation of the Cannabis Engagement Assessment
Bibliographic record
Abstract
Introduction: Methods of cannabis engagement have proliferated in recent years, which many self-report measures do not adequately capture. There is a clear need for self-report measures that capture current patterns of cannabis use across a range of methods, and that can be used to track changes over time. The current study developed the Cannabis Engagement Assessment (CEA), a self-report measure of past month cannabis use across dry flower, concentrate, and edible products. Methods: A sample of 349 participants from the undergraduate student population and broader community were recruited. To examine convergent validity of the CEA, participants completed measures of cannabis engagement, cannabis misuse, and use-related problems. To assess divergent validity, participants also completed measures of depression and alcohol use problems. Criterion and test-retest reliability were examined in a subset of 65 participants who re-completed the CEA and a timeline follow-back interview (TLFB). Results: Indicators of cannabis use frequency and quantity showed good convergence with measures of cannabis use patterns, problematic engagement, and cannabis use-related problems. Divergent validity of the CEA was supported by lower associations with alcohol use problems and depression symptoms. The CEA also showed good test-retest reliability and convergence with estimates of frequency and quantity of cannabis use from the TLFB. Conclusions: The CEA is a viable self-report measure of cannabis use that is representative of current patterns of recreational cannabis engagement. Its focus on cannabis use in the preceding 30 days also lends itself to measuring changes in use over time.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".