The association of relative deprivation, interpersonal relationships, and problematic social media use in 39 countries/regions: Does school contextual inequality matter?
Bibliographic record
Abstract
Background: Social media use among adolescents has rapidly expanded, raising concerns about problematic social media use (PSMU). Few studies have examined how relative deprivation (RD) and contextual inequality shape PSMU. We explain whether four key interpersonal relationships (relationships with teachers, classmates, and family, and bullying victimization) mediate the link between RD and PSMU and whether school-level inequality moderates these associations. Method: The data was from the 2017/2018 Health Behavior in School-aged Children (190,707 adolescents, 5,576 schools, 39 countries/regions). The independent variable was RD, calculated via the Yitzhaki index, and the dependent variable was PSMU. Four interpersonal relationships (teacher, classmate, and family relationships, and bullying victimization) were assessed as mediators. Multilevel mediation models were used to evaluate the mediating roles of these relationships. We then extracted each country/region's indirect effects for cross-national comparisons. The Johnson-Neyman method was used to assess the moderating effect of contextual inequality. Results: Although RD had no direct effect on PSMU, higher RD was associated with poorer teacher, classmate, and family relationships and heightened bullying victimization. These then predicted increased PSMU. Contextual inequality strengthened RD's adverse association with family relationships and magnified the associations of family relationships and bullying on PSMU. Cross-national analyses revealed stable mediating effects of teacher and classmate relationships. Conclusions: We highlight RD's associations with PSMU primarily through interpersonal pathways, with contextual inequality amplifying them. Targeted interventions that address both socioeconomic disparities and interpersonal dynamics can help mitigate PSMU. These outcomes underscore the need for policies that reduce economic gaps and foster supportive relationships.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".