Cross-National Trends in Adolescents Psychological and Somatic Complaints Before and After the Onset of COVID-19 Pandemic
Bibliographic record
Abstract
PURPOSE: Building on research suggesting that the COVID-19 pandemic may have led to an exacerbation of deteriorating trends in mental health among adolescents, this paper examined trends in adolescents' psychological and somatic complaints across 35 countries from 2010 to 2022, and tested trends in sociodemographic inequalities in these outcomes between 2018 and 2022. METHODS: Using data from 792,606 adolescents from 35 countries (51% girls; mean age = 13.5; standard deviation 1.6) across four Health Behaviour in School-aged Children surveys (2010, 2014, 2018, 2022), hierarchical multilevel models estimated cross-national trends in adolescent psychological and somatic complaints. We tested whether observed values in 2022 were in line with predicted values based on 2010-2018 linear trends. Finally, moderation effects of age, family affluence, and family structures on the outcomes were tested (2018-2022). RESULTS: Both girls and boys showed substantially higher levels of psychological complaints in 2022 compared with the predicted values. For somatic complaints, higher levels than predicted in 2022 were observed only in girls. Moderation analyses revealed an increase from 2018 to 2022 in age gaps and a narrowing in the socioeconomic gap for both outcomes. Also, there was a widening gap between adolescents living with 2 parents and those living in a single parent household in 2022 compared to 2018. DISCUSSION: Cross-national increases in adolescent psychological and somatic complaints were higher than expected in 2022, based on previous trends. Magnitudes of change varied across different sociodemographics groups, with implications for pre-existing mental health inequalities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".