Factors related to adolescents' estimation of peer smoking prevalence
Bibliographic record
Abstract
Although adolescents who overestimate peer smoking prevalence are more likely to smoke, little research has focused on the factors associated with why the majority of adolescents overestimate peer smoking rate. The purpose of this study was to examine demographic, social, environmental and behavioural characteristics related to overestimation of peer smoking prevalence among secondary school students. The current study analysed data collected in two Canadian studies that used the Tobacco Module of the School Health Action, Planning and Evaluation System, a school-based questionnaire. One study surveyed 23 458 students (Grades 9-13) in 29 schools during 2001-02, and the other surveyed 25 452 students in 39 schools in 2003. Results of multiple logistic regression indicate that grade, gender, close friends' smoking, seeing smoking at school, family members' smoking, smoking in the home and smoking status have a clear association with overestimation; school smoking rate and susceptibility to smoking show a tentative relationship and warrant further study. Other factors may also be important for prevalence estimation, and further research is needed to identify these factors. Since adolescents tend to overestimate peer smoking prevalence and perceived prevalence is in turn linked to smoking behaviour, interventions should focus on creating realistic perceptions of smoking prevalence.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".