Structural gender inequality and gender differences in adolescent substance use: A multilevel study from 45 countries
Bibliographic record
Abstract
Although adolescent substance use has declined, young people’s tobacco and alcohol use levels are still among the highest in Europe and North America. Historically, boys reported higher levels of substance use than girls; however, in recent decades gender convergence in adolescent substance use was observed in some, mostly Western, countries. Previous research has shown associations between societal gender inequality and gender differences in some externalizing behaviors in adolescents. Therefore, there is a need to go beyond individual-level associations and apply a socio-ecological perspective when examining gender differences in adolescent substance use. This study examines whether gender differences in adolescent substance use relate to societal gender inequality. Current and lifetime substance use (i.e., alcohol drinking, drunkenness, cigarette smoking) were measured in 11-, 13 and 15-year-olds in the 2017/18 Health Behaviour in School-aged Children study (n=224,876). Individual data were linked to national gender inequality (Gender Inequality Index, 2018) in 45 countries and regions, and their association was tested using mixed effects (multilevel) logistic regression models Large cross-national variations were observed in gender differences in substance use. Greater gender inequality at country level was associated with heightened gender differences in substance use, however with different effects depending on the substance type. For most substances, few gender differences emerge in countries characterized by low levels gender inequality. The largest gender differences were observed in countries characterized by high gender inequality Societal gender inequality reflects social and cultural norms that relate to adolescents’ engagement with substance use. Public health policy should target societal factors that impact on young people’s behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".