Mapping adolescent problematic social media use patterns across 41 countries/regions: A multilevel latent class analysis with social determinants
Bibliographic record
Abstract
BACKGROUND: Problematic social media use (PSMU) represents a growing concern among adolescents globally. While existing variable-centered research has provided valuable insights into PSMU determinants, person-centered approaches can offer complementary perspectives by identifying behavioral heterogeneity within populations and examining how social determinants differentially affect distinct subgroups across diverse national contexts. OBJECTIVES: This study aimed to (1) identify distinct PSMU behavioral classes at the individual level, (2) classify countries/regions based on PSMU prevalence patterns, and (3) examine how multilevel social determinants predict class membership. METHODS: Data were from 171,447 adolescents across 41 countries/regions. Multilevel latent class analyses were first conducted on nine dimensions of PSMU to identify distinct classes at individual and national levels. Individual-level measures incorporated social relationship quality, health behaviors, and economic deprivation. National-level indicators included economic development, education, income, inequalities, and cultural values. Multinomial regressions were performed to examine associations between social determinants and PSMU class membership. RESULTS: Three distinct individual-level PSMU classes emerged: Low Problematic Use Class (58.0%), Moderate Problematic Use Class (37.6%), and High Problematic Use Class (4.3%). Countries/regions are clustered into three categories: Low Prevalence Region (31.7%), Moderate Prevalence Region (39.0%), and High Prevalence Region (29.2%). At the individual level, positive social relationships and healthy lifestyles demonstrated protective effects against problematic use, while economic deprivation increased risk. At the national level, educational inequality, secular values, and gender inequality significantly increased the likelihood of countries belonging to the High Prevalence Region. CONCLUSION: Adolescent PSMU manifests heterogeneously across individuals and exhibits geographic variation. This study highlights the necessity of multilevel, differentiated intervention strategies, emphasizing that countries/regions should develop policies aligned with their specific characteristics to foster supportive environments for adolescent digital 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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".