The Relationship of Field of Study to Current Smoking Status among College Students.
Bibliographic record
Abstract
Problem: No research to date has examined smoking rates among the different fields of study and smoking among college students. Thus, this study aimed to determine if smoking prevalence vary among students in the different fields of study. Method: An online health behavior survey was administered to 25,000 students (n=6,492; 26% response rate). Results: Smoking prevalence ([greater than or equal to] 1 cigarette in past 30 days) among our sample was 28.5%. Field of study was associated with smoking (p Conclusions: Students within certain fields of study are at high risk for smoking. Research should further validate these findings. Pending further support, additional work should investigate reasons for differential smoking rates and aim to develop cessation programs targeting high-risk students. ********** Young adulthood is a critical transition period in cigarette use (Bachman, Wadsworth, O'Malley, Johnston, & Schulenberg, 1997; Chert & Kandel, 1995). While first experimentation with cigarettes occurs early in life for the majority of individuals, increased frequency of smoking and establishment of regular or heavy cigarette use often occurs during the young adult years (Everett, Husten et al., 1999; Everett, Warren et al., 1999). Encouraging smoking cessation early in life is crucial to help individuals avoid many of the harms related to smoking (Doll, Peto, Boreham, & Sutherland, 2004; Orleans, 2007). Effective strategies targeting occasional or intermittent young adult smokers are needed to interrupt the progression to regular smoking behavior and the development of nicotine dependence. Colleges and universities provide a venue for intervening with young adults that smoke or may initiate smoking. In 2004, U.S. colleges and universities enrolled over 14 million students, including nearly 40% of the U.S. population between the ages of 18 and 24 (U.S. Census Bureau, 2006). Hammond (Hammond, 2005), analyzing Canadian Census data (with comparable rates of smoking and college enrollment to the U.S.), noted that students represent a larger proportion of young adult smokers than any other occupation or employment sector (e.g., 15 % of young adult smokers worked in the service/sales sector; Hammond, 2005). Given the substantial proportion of young adults that attend college, this is an important target group for smoking cessation interventions. Recent research has examined groups of young adults at risk for smoking and other deleterious health behaviors (Bader, Travis, & Skinner, 2007; Green et al., 2007; Husten, 2007). In 2005, smoking prevalence (i.e., having smoked at least 100 cigarettes in lifetime and reporting smoking on at least some days) among young adults (aged 18-24) was 24% (CDC, 2006). A recent study by Solberg and colleagues (Solberg et al., Asche, Boyle, McCarty, & Thoele, 2007) investigated the prevalence of current regular smoking and infrequent smoking among young adults with respect to status as a college student and the type of college attended (i.e., two-year vs. four-year). Non-students or those without a college education demonstrated the highest rates of smoking (47.5% current, 2.4% infrequent; Solberg et al., 2007). Those attending technical colleges or two-year institutions also reported higher rates of smoking (30.6% current; 3.0% infrequent) in comparison to those attending four-year schools (16.4% current, 4.3% infrequent) (Solberg et al., 2007). Estimates from the 2005 National Survey on Drug Use and Health are higher (NSDUH, 2006), reporting that approximately 25% of college students are current 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".