Foreign Medical Students in Eastern Europe: Knowledge, Attitudes and Beliefs about Medical Cannabis for Pain Management
Bibliographic record
Abstract
Objective: To assess the knowledge, attitudes, and beliefs of foreign students toward the use of medical cannabis (MC) for pain management. Methods: This study uses data collected from 549 foreign students from India (n = 289) and Middle Eastern countries mostly from Egypt, Iran, Syria, and Jordan (n = 260) studying medicine in Russia and Belarus. Data collected from Russian and Belarusian origin medical students (n = 796) were used for comparison purposes. Pearson’s chi-squared and t-test were used to analyze the data. Results: Foreign students’ country of origin and gender statuses do not tend to be correlated with medical student responses toward medical cannabis use. Students from Russia and Belarus who identified as secular, compared to those who were religious, reported more positive attitudes toward medical cannabis and policy change. Conclusions: This study is the first to examine the attitudes, knowledge, and beliefs toward medical cannabis among foreign students from India and Middle Eastern countries studying in Russia and Belarus, two countries who oppose its recreational and medicine use. Indian and Middle Eastern students, as a group, tend to be more supportive of MC than their Russian and Belarusian counterparts. These results may be linked to cultural and historical reasons. This study provides useful information for possible medical and allied health curriculum and education purposes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".