Survey of subjective effects of smoking while drinking among college students
Bibliographic record
Abstract
Prevalence of tobacco use among the college-aged population is approximately 30%; a significant percentage of students initiate use or transition to regular use during their college years. This study examined the relationship between drinking and smoking rates, subjective reactivity of concurrent effects of alcohol and tobacco use, and expectations of smoking while under the influence of alcohol in first-year college students. The sample consisted of ever-smokers (n=217), who had smoked at least once in the past year, with a mean age of 19.67 years. Weekly alcohol consumption was 18.53 standard drinks per week, with 2.95 drinking episodes per week. Of the sample, 54% were classified as smokers (smoked more than 100 cigarettes in their lifetime) and 46% were classified as experimenters (smoked less than 100 cigarettes in their lifetime). Results demonstrated that 74% of all smoking episodes occurred while under the influence of alcohol. Smokers had higher levels of alcohol use and reported greater subjective effects from the simultaneous use of alcohol and tobacco. Smokers also were more likely to generate expectancies acknowledging an increase in smoking while drinking and for smoking to enhance reinforcement from alcohol. Experimenters were most likely to report positive reinforcement from smoking while under the influence of alcohol. Overall, smokers experienced stronger subjective effects of concurrent alcohol and tobacco use; however, results suggest that smoking while under the influence of alcohol is a positive experience even for relatively inexperienced smokers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".