Predictors of Salvia divinorum Use Among a National Sample of Entering First-year U.S. College Students
Bibliographic record
Abstract
Objective: Past epidemiological studies have revealed that 18- to 25-year-olds have the highest rate of Salvia divinorum (salvia) use. This study examines predictors of salvia use among a large national sample of incoming first-year college students attending 144 academic institutions.Method: Each institution instructed their entering first-year students to complete an online alcohol course. A total of 7,314 randomly selected students completed a version of the course’s baseline survey that included questions about salvia use.Results: Salvia use in the past two weeks was reported by 3.5%. In a multivariate model, past-two-week salvia use was more common among students who were male, non-White, and had an absent father; this study did not correct for multiple statistical tests, and therefore, these results may be spurious. Salvia use and use of cigarettes and marijuana were strongly related in bivariate analyses. Current drinkers were approximately two times more likely to use salvia in the past two weeks. More than a third of those reporting past-two-week salvia use reported using salvia while under the influence of marijuana in the past month.Discussion: This study is the first to examine salvia and other substance use over the past two weeks and explores the use of salvia under the influence of marijuana. Students being disciplined for marijuana-related offenses should be questioned about the concomitant use of salvia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".