Associations of Trying to Lose Weight, Weight Control Behaviors, and Current Cigarette Use Among US High School Students
Bibliographic record
Abstract
BACKGROUND: Approximately one-quarter of high school students currently use cigarettes. Previous research has suggested some youth use smoking as a method for losing weight. The purpose of this study was to describe the association of current cigarette use with specific healthy and unhealthy weight control practices among 9th-12th grade students in the United States. METHODS: Youth Risk Behavior Survey data (2005) were analyzed. Behaviors included current cigarette use, trying to lose weight, and current use of 2 healthy and 3 unhealthy behaviors to lose weight or to keep from gaining weight. Separate logistic regression models calculated adjusted odds ratios (AORs) for associations of current cigarette use with trying to lose weight (Model 1) and the 5 weight control behaviors, controlling for trying to lose weight (Model 2). RESULTS: In Model 1, compared with students who were not trying to lose weight, students who were trying to lose weight had higher odds of current cigarette use (AOR = 1.30, 95% CI: 1.15-1.49). In Model 2, the association of current cigarette use with the 2 healthy weight control behaviors was not statistically significant. Each of the 3 unhealthy weight control practices was significantly associated with current cigarette use, with AORs for each behavior approximately 2 times as high among those who engaged in the behavior, compared with those who did not. CONCLUSION: Some students may smoke cigarettes as a method of weight control. Inclusion of smoking prevention messages into existing weight management interventions may be beneficial.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".