The development of a Cannabis Knowledge Assessment Tool (CKAT)
Bibliographic record
Abstract
BACKGROUND: Misconceptions about the health risks of cannabis remain prevalent, indicating the need to improve public health messaging and determine the effectiveness of educational programming. Our objective was to develop a standardized questionnaire to measure knowledge about cannabis in the context of cannabis legalization. METHODS: A Cannabis Knowledge Assessment Tool (CKAT) was created using the Delphi method. A purposive sample of healthcare professionals, policymakers, academics, patients, and students served as the content and development experts. Principal component analysis from the codes identified from open-ended feedback guided the item development. Upon completion, the CKAT was administered as a pre- and post-test in four schools (7th and 9th Grade) in Canada. The data were analysed to determine whether knowledge scores changed after participating in a cannabis education program. RESULTS: Twenty-four experts initially participated in the Delphi process and 18 (75% retention) continued throughout. Principal component analysis identified 3 domains: 1) effects of cannabis on the individual, 2) general information about cannabis, and 3) cannabis harm reduction. The final questionnaire consisted of 16 multiple-true-false questions (64 items) and received a Flesch-Kincaid Grade Level of 6.3, and a SMOG index score of 7.6. The CKAT was completed by 132 students; seventy-three 7th grade and fifty-nine 9th grade students. The baseline mean CKAT score was 46.2 (SD:5.5), which increased to 50.7 (SD:4.6) after the cannabis educational program (p<0.05). CONCLUSIONS: A novel tool to measure knowledge of cannabis was developed and piloted in 7th grade and 9th grade students. Future studies are required to test usability and validity of the CKAT in other contexts.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".