Cigarette smoking and associated health risks among students at five universities
Bibliographic record
Abstract
INTRODUCTION: While most college students and other young adults who smoke fall into the light and intermittent smoking (LITS) category, they remain at risk for tobacco dependence and other adverse health effects from their smoking. This study examines smoking patterns, tobacco dependence, and other health variables among students at five universities to better understand how to identify and address tobacco use and related risks in a college health clinic setting. METHODS: A health screening survey was completed by 2,091 college and graduate student volunteers seeking routine care at their university health centers or participating in a health class. Independent health variables were analyzed descriptively and in regression analyses with three levels of smoking (none, non-daily, and daily) and tobacco dependence to determine predictors and associated risks. RESULTS: Nearly a quarter of students reported any current smoking, 41% of whom reported smoking less than 1 cigarette/day (cpd). Of the daily smokers, 80% smoked less than 10 cpd but 45% met criteria for tobacco dependence. Any smoking was associated with high-risk alcohol use, risky driving, relational abuse, depression, less exercise, and utilization of emergency and mental health services. In regression analyses, students who experienced depression had more than double the odds of being dependent smokers (odds ratio [OR] = 2.32), as did those who reported abuse (OR = 2.07) or sought mental health counseling (OR = 2.09). DISCUSSION: Student health providers should be alerted to the multiple risks and comorbidities that occur among all smokers, including LITS, and intervene concurrently to help prevent or mitigate adverse outcomes that result from these conditions and behaviors.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".