Clusters of Contemporary Risk and Their Relationship to Mental Well-Being Among 15-Year-Old Adolescents Across 37 Countries
Bibliographic record
Abstract
PURPOSE: Adolescents' mental well-being has become a growing public health concern. Adolescents' daily lives and their engagement in risks have changed dramatically in the course of the 21st century, leading to a need to update traditional models of risk to include new exposures and behaviors. To date, studies have examined the relationship between (mainly traditional) risk behaviors and adolescent mental well-being or looked at risk factors that jeopardize mental well-being such as lack of social support but have not combined them together to highlight the most significant risks for adolescent mental well-being today. The present study included new and traditional risk behaviors and risk factors, robustly derived an empirically based model of clusters of risk, and examined the relative association of these clusters to adolescent mental well-being. METHODS: Data from the 2017-2018 Health Behaviours in School-aged Children study were used. The sample included 32,884 adolescents (51.7% girls) aged 15 years from 37 countries and regions. The principal component analysis was used to determine the existence of clusters of risk, using 21 items related to adolescent mental well-being that included both risk behaviors (e.g., substance use) and risk factors (e.g., peer support). Analysis was conducted in both a randomly split training and test set and in gender separate models. Mixed-effects logistic regressions examined the association between clusters of risk and mental well-being indices (low life satisfaction and psychosomatic complaints). RESULTS: Seven clusters of risk were identified: substance use and early sex, low social support, insufficient nutrition, bullying, sugary foods and drinks, physical health risk, and problematic social media use (SMU). Low social support and SMU were the strongest predictors of low life satisfaction (odds ratios = 2.167 and 1.330, respectively) and psychosomatic complaints (odds ratio = 1.687 and 1.386, respectively). Few gender differences in predictors were found. Exposure to bullying was somewhat more associated with psychosomatic complaints for girls, whereas physical health risk was associated with reduced relative odds of low life satisfaction among boys. Split-sample validation and out-of-sample prediction confirmed the robustness of the results. CONCLUSIONS: The results highlight the importance of contemporary clusters of risk, such as low social support and SMU in the mental well-being of young people and the need to focus on these as targets for prevention. We propose that future studies should use composite risk measures that take into account both risk behaviors and risk factors to explain adolescents' mental well-being.
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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) | 0.001 | 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 | 0.001 |
| 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".