Development of a cannabis health literacy questionnaire: preliminary validation using the Rasch model
Bibliographic record
Abstract
Abstract Background As cannabis becomes more integrated into Canadian society for medical and non-medical purposes, public health efforts have aimed to enhance public awareness and knowledge of the potential risks associated with cannabis use. However, no validated or established method to measure cannabis health literacy exists, limiting the ability to evaluate the impacts of public awareness initiatives. We aimed to develop and preliminarily validate a cannabis health literacy questionnaire (CHLQ) designed to measure an individual’s knowledge, understanding and utilization of health and safety information related to cannabis. Methods The CHLQ was developed using existing health literacy domains and alcohol health literacy attributes as a framework. The questions were informed by extensive literature, item-response theory principles and input from stakeholders and people who use cannabis. The CHLQ includes four dimensions: knowledge of cannabis, knowledge of risks, understanding of associated risks and harms, and the ability to seek, access and use cannabis information. Adult participants were recruited through an online survey platform and social media. The questionnaire was refined and revised over three iterations using the Rasch analysis. Our preliminary validation process analyzed the CHLQ’s reliability and construct validity examining separation reliability, item difficulty, item fit statistics and unidimensionality. Results A total of 1035 individuals across Canada completed our CHLQ. Each dimension of the CHLQ, had a well-distributed range of question difficulties. Across the four dimensions, item separation ranged from 9.93 to 17.29, and item reliability ranged from 0.99 to 1.00. Person separation ranged from 0.99 to 1.88, while person reliability ranged from 0.49 to 0.78. Most questions fit within the model, and unidimensionality was supported for all dimensions. Each dimension is scored separately with high scores indicating high knowledge or understanding for the respective domain. Raw scores for each dimension can be transformed to a linear Rasch score. Conclusions The CHLQ is a preliminary, multi-dimensional tool designed to measure cannabis health literacy for educational and research use. It demonstrates promising psychometric properties and provides an initial framework to inform public health efforts. Further validation in diverse population and settings is needed. The CHLQ provides foundation for future research, evaluation and public education efforts related to cannabis use.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".