Is educational differentiation associated with smoking and smoking inequalities in adolescence? A multilevel analysis across 27 European and North American countries
Bibliographic record
Abstract
This study aims to determine whether educational differentiation (i.e. early and long tracking to different school types) relate to socioeconomic inequalities in adolescent smoking. Data were collected from the WHO-Collaborative 'Health Behaviour in School-aged Children (HBSC)' study 2005/2006, which included 48,025 15-year-old students (Nboys = 23,008, Ngirls = 25,017) from 27 European and North American countries. Socioeconomic position was measured using the HBSC family affluence scale. Educational differentiation was determined by the number of different school types, age of selection, and length of differentiated curriculum at the country-level. We used multilevel logistic regression to assess the association of daily smoking and early smoking initiation predicted by family affluence, educational differentiation, and their interactions. Socioeconomic inequalities in both smoking outcomes were larger in countries that are characterised by a lower degree of educational differentiation (e.g. Canada, Scandinavia and the United Kingdom) than in countries with higher levels of educational differentiation (e.g. Austria, Belgium, Hungary and The Netherlands). This study found that high educational differentiation does not relate to greater relative inequalities in smoking. Features of educational systems are important to consider as they are related to overall prevalence in smoking and smoking inequalities in adolescence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".