Susceptibility to Smoking Among White and Chinese Nonsmoking Adolescents in Canada
Bibliographic record
Abstract
OBJECTIVE: To document the prevalence of susceptibility to smoking among a sample of White/Caucasian and Chinese Canadian adolescent nonsmokers, and to explore the factors that might explain who is susceptible to smoking. DESIGN: This study used a secondary analysis of data from students participating in the British Columbia Youth Survey on Smoking and Health in 2001/2002. SAMPLE: The sample included 1,870 10th and 11th graders who were nonsmokers with either a White or a Chinese ethnic background. MEASUREMENTS: Questionnaire data consisted of demographic and social factors, previous smoking experience, and susceptibility to smoking. RESULTS: Among the total sample, 27.7% were susceptible to smoking. Multivariate logistic regression analysis revealed that 11th graders were less susceptible than 10th graders (odds ratio [OR]=0.80, 95% confidence interval [CI] 0.64-0.99), and girls were more susceptible than boys (OR=1.32, 95% CI 1.05-1.65). Ethnicity did not help to explain susceptibility to smoking in this study. CONCLUSIONS: The findings indicated the effects of gender and grade on predicting susceptibility to smoking. Even though the Chinese Canadian adolescents had the same risk of susceptibility to smoking as White/Caucasians, the factors that put them at risk may be different, which suggests the need to further examine the ethnic-specific predictors of susceptibility to 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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".