Drug Knowledge, Attitudes, Beliefs and Use among Chinese International Students on the West Coast of the U.S. and Canada.
Bibliographic record
Abstract
This exploratory study examined the knowledge, experience, attitude, and perceptions of drugs reported by international students from mainland China (N = 97) studying on the West Coast of the U.S. and Canada. Chinese students currently constitute the largest group of international students at universities on the West Coast, which is also the epicenter of a major shift in the legal and cultural status of cannabis. Participants’ knowledge, exposure, use, attitudes, and norms of peers’ use of five drugs (cannabis, heroin, ketamine, methamphetamine, and Adderall) were elicited through an online survey. Data were analyzed quantitatively (descriptive statistics, means of paired samples, correlations). Many correlations existed between participants’ attitudes, comments they heard others make, and their beliefs about their peers’ drug use. Participants were most familiar with cannabis; their exposure to others’ comments about and use of cannabis in North America were starkly different than what they had experienced in China. About 10% of the sample had tried cannabis in North America. Students were familiar with heroin and methamphetamine from their experiences in China, and those drugs were viewed extremely negatively. Use of and exposure to ketamine and Adderall were rare, and all drugs were viewed much more negatively when asked in the context of living in China than in North America. Given the current and recent changes in drug laws around the world and students’ reported experience with cannabis, this study underlines the urgency of educating international students regarding drug use as well as informing educational policy at the university level.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".