Beyond Smoking Prevalence: Exploring the Variability of Associations between Neighborhood Exposures across Two Nested Spatial Units and Two-Year Smoking Trajectory among Young Adults
Bibliographic record
Abstract
Young adults have the highest prevalence of smoking amongst all age groups. Significant uptake occurs after high school age. Although neighborhood exposures have been found to be associated with smoking behavior, research on neighborhood exposures and the smoking trajectories among young adults, and on the role of geographic scale in shaping findings, is scarce. We examined associations between neighborhood exposures across two nested, increasingly large spatial units and smoking trajectory over two years among young adults living in Montreal, Canada. A sample of 2093 participants aged 18-25 years from the Interdisciplinary Study of Inequalities in Smoking (ISIS) was surveyed. The dependent variable was self-reported smoking trajectory over the course of two years. Residential addresses, data on presence of tobacco retail outlets, and the presence of smoking accommodation facilities were coded and linked to spatial units. Three-level multinomial models were used to examine associations. The likelihood of being a smoker for 2+ years was significantly greater among those living in larger spatial unit neighborhoods that had a greater presence of smoking accommodation. This association was not statistically significant at the smaller spatial units. Our findings highlight the importance of studying young adults' smoking trajectories in addition to static smoking outcomes, and point to the relevance of considering spatial scale in studies of neighborhoods and smoking.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".