Smoking Trajectory Classes and Impact of Social Smoking Identity in Two Cohorts of U.S. Young Adults
Bibliographic record
Abstract
This study describes cigarette smoking trajectories, the influence of social smoker self-identification (SSID), and correlates of these trajectories in two cohorts of U.S. young adults: a sample from the Chicago metropolitan area (Social Emotional Contexts of Adolescent and Young Adult Smoking Patterns [SECAP], n = 893) and a national sample (Truth Initiative Young Adult Cohort Study [YA Cohort], n = 1,491). Using latent class growth analyses and growth mixture models, five smoking trajectories were identified in each sample: in SECAP: nonsmoking ( n = 658, 73.7%), declining smoking ( n = 20, 2.2%), moderate/stable smoking ( n = 114, 12.8%), high/stable smoking ( n = 79, 8.9%), and escalating smoking ( n = 22, 2.5%); and in YA Cohort: nonsmoking ( n = 1,215, 81.5%), slowly declining smoking ( n = 52, 3.5%), rapidly declining smoking ( n = 50, 3.4%), stable smoking ( n = 139, 9%), and escalating smoking ( n = 35, 2.4%). SSID was most prevalent in moderate/stable smoking (35.5% SECAP), rapidly declining smoking (25.2% YA Cohort), and nonsmoking. Understanding nuances of how smoking identity is formed and used to limit or facilitate smoking behavior in young adults will allow for more effective interventions to reduce tobacco use.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".