Using ecological momentary assessment to quantify Δ‐9‐tetrahydrocannabinol and cannabidiol use across different forms of cannabis: Feasibility in a sample of Canadian young adults reporting frequent cannabis use
Bibliographic record
Abstract
AIMS: To establish the feasibility of using ecological momentary assessment (EMA) to estimate total quantities of Δ-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) used across different forms of cannabis, and to assess the predictive validity of THC estimates for predicting acute cannabis-related consequences. DESIGN: 14-day EMA using a smartphone application to assess cannabis use in real time. SETTING: Canada. PARTICIPANTS: Targeted sample of n = 42 young adults (59.52% women, mean age 25 years) reporting frequent cannabis use. MEASUREMENTS: Surveys completed immediately prior to cannabis use assessed the quantities, THC content and CBD content of various forms of cannabis to be used in the current session; participants also uploaded photos of the cannabis product labels when available. Surveys administered at fixed times throughout the day (84.81% completion rate) assessed acute cannabis-related consequences. FINDINGS: Participants completed a total of 786 pre-cannabis surveys, of which 79.39% and 77.35% contained sufficient information to calculate total THC and CBD (in milligrams), respectively. High agreement was observed between participant-entered THC and CBD contents and those shown in corresponding photos of cannabis product labels. Aggregating across all products used, participants reported using an average of 141.41 [standard deviation (SD) = 224.62, range = 0.00-2000.00] milligrams of THC (i.e. 28.28 standard five-milligram units) and 7.53 (SD = 34.87, range = 0.00-484.22) milligrams of CBD per day. Multilevel models revealed that participants were more likely to report acute negative consequences following sessions when their estimated THC use was higher than their typical THC use. At the between-person level, participants reporting more THC use on average across sessions were less likely to report negative consequences overall. CONCLUSIONS: Using ecological momentary assessment to estimate total quantities of Δ-9-tetrahydrocannabinol and cannabidiol used across different forms of cannabis appears to be feasible, with preliminary predictive validity for acute negative cannabis-related consequences.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".