Global change in adolescent social media use (2018–2022): An ecological analysis across 28 countries
Bibliographic record
Abstract
Given growing concerns about the role of social media in adolescents' lives, especially after the COVID-19 pandemic, this study investigates changes in social media use (SMU) between 2018 and 2022 across 28 countries. The main aim is to detect any change in adolescents' SMU, as reflected in the rates of four categories of social media users (i.e., non-active users, active users, intense users, and problematic users) between 2018 and 2022, and explore interactions with several individual, social and national factors involved in possible changes. Data were gathered from 326,397 adolescents aged 11, 13, and 15 from 28 countries involved in the Health Behaviour in School-aged Children study. Results showed that there was a modest decline in the prevalence of non-active users (by 2.8 pp (percentage points)), active users (by 0.8 pp), and intense social media users (by 1.6 pp), accompanied by a 2.8 pp increase in the prevalence of problematic social media users. Overall, these temporal changes were confirmed across the participating countries. Girls, younger adolescents, those with low socio-economic status (SES), and with medium-low family and peer support experienced stronger temporal increases in reported problematic SMU. Younger adolescents also showed a stronger temporal decrease of non-active SMU. A significant moderation effect of available national-level indicators (i.e., GINI, GII, Stringency Index, ICT access) was identified with respect to temporal changes in problematic SMU. These changes should be interpreted within the context of today's increasingly technologized world. Results are discussed with a global preventive perspective.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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".