Worldwide increases in adolescent loneliness
Bibliographic record
Abstract
INTRODUCTION: Several studies have documented increases in adolescent loneliness and depression in the U.S., UK, and Canada after 2012, but it is unknown whether these trends appear worldwide or whether they are linked to factors such as economic conditions, technology use, or changes in family size. METHODS: The Programme for International Student Assessment (PISA) survey of 15- and 16-year-old students around the world included a 6-item measure of school loneliness in 2000, 2003, 2012, 2015, and 2018 (n = 1,049,784, 51% female) across 37 countries. RESULTS: School loneliness increased 2012-2018 in 36 out of 37 countries. Worldwide, nearly twice as many adolescents in 2018 (vs. 2012) had elevated levels of school loneliness. Increases in loneliness were larger among girls than among boys and in countries with full measurement invariance. In multi-level modeling analyses, school loneliness was high when smartphone access and internet use were high. In contrast, higher unemployment rates predicted lower school loneliness. Income inequality, GDP, and total fertility rate (family size) were not significantly related to school loneliness when matched by year. School loneliness was positively correlated with negative affect and negatively correlated with positive affect and life satisfaction, suggesting the measure has broad implications for adolescent well-being. CONCLUSIONS: The psychological well-being of adolescents around the world began to decline after 2012, in conjunction with the rise of smartphone access and increased internet use, though causation cannot be proven and more years of data will provide a more complete picture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".