Evaluating the use and perceptions of cannabis and vaping post-cannabis legalisation in people with cystic fibrosis and CFTR-related disorder: survey results from a large Canadian adult cystic fibrosis clinic
Bibliographic record
Abstract
BACKGROUND: This study characterised the use and perceptions of cannabis and vaping in people with cystic fibrosis (CF) and CF transmembrane conductance regulator (CFTR)-related disorder followed by a large Canadian adult CF clinic. It also aimed to assess whether cannabis legalisation in Canada affected perceived benefits and harms, and whether media attention regarding e-cigarette or vaping product use-associated lung injury (EVALI) affected perceptions of vaping. METHODS: An electronic questionnaire was emailed to all clinic patients on 23 April 2021, and remained open until 28 October 2021. RESULTS: 110 individuals completed the questionnaire, of whom 43% identified as a current user of cannabis. As a result of legalisation, 14% of respondents reported change in their perceptions of cannabis, primarily related to decreased stigma and increased awareness of medical indications and potential side effects. Cannabis was reported as being used medically for 85% of current users, with stress, insomnia/lack of sleep, and anxiety being the most common symptoms treated; the majority reported it to be somewhat or very effective to manage symptoms. Overall, 33% of respondents had tried vaping, but only 7% considered themselves current vapers. For 45% of respondents, the 2019 EVALI epidemic was reported to have changed perceptions of potential short-term and long-term effects associated with vaping, with increased awareness of potential harms. CONCLUSIONS: Cannabis use was common, with a reported increase since its legalisation in this population. EVALI media attention was reported to increase awareness for potential harms associated with vaping. CF healthcare providers are well positioned to provide education and support so patients can make informed decisions about cannabis use and vaping.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".