Co-use of cannabis with commonly used licit and illicit drugs
Bibliographic record
Abstract
Background: Following the recent legalization of medical cannabis in Canada, and many other countries around the world, people are turning to this drug for both medical and recreational reasons. Naturally, as human’s age, many rely on medication to maintain a better quality of life. Surveys show that, once legal, there will be an increase in cannabis consumption. Many adverse health reactions may occur by concurrently taking cannabis and other medications. Methods: A survey was distributed in-person throughout Vancouver targeting people who do not consume cannabis. The same survey was distributed in Vancouver, but to people coming out of dispensaries, targeting people who do consume cannabis. The survey consisted of seven knowledge questions asking about possible adverse drug interactions occurring between cannabis and commonly used licit and illicit drugs. A chi-square analysis was used to compare knowledge of users and non-users of cannabis. Results: Both users and non-users seemed to be most knowledgeable on the interaction between cannabis and alcohol; 39 out of 57 users (68%) and 23 out of 30 non-users (77%) gave the correct response. As for all the other interactions, neither group was very knowledgeable. The distribution of questions that were answered incorrectly seemed evenly spread between the two groups. The knowledge between users and non-users were significantly different when participants were asked on the possible adverse reactions between cannabis and opioid drugs (p=0.005), and cannabis and sedative drugs (p=0.002). In these cases, cannabis users were more knowledgeable about cannabis interactions than non-users. Conclusion: This study indicates that the general public is not very knowledgeable on the possible adverse reactions that may come about as a result of mixing cannabis and other commonly used licit and illicit drugs. Actions should be taken to provide the public with tools that will aid them in making the right decision when thinking about concurrently using cannabis and other licit and/or illicit drugs.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".