Mental health effects prevalence in children and adolescents during the COVID‐19 pandemic: A systematic review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic health crisis has changed household and school routines leaving children and adolescents without important anchors in life. This, in turn, can influence their mental health, changing their behavioral and psychological conditions. AIMS: To systematically review the literature to answer the question: "What is the worldwide prevalence of mental health effects in children and adolescents during the COVID-19 pandemic?". METHODS: Embase, Epistemonikos database, LILACS, PsycINFO, PubMed, Scopus, Web of Science, and World Health Organization Global literature on coronavirus disease were searched. Grey literature was searched on Google Scholar, Grey Literature Report, and Preprint server MedRxiv. Observational studies assessing the prevalence of mental health effects in children and adolescents during the COVID-19 pandemic were included. Four authors independently collected the information and assessed the risk of bias of the included studies. RESULTS: From a total of 11,925 identified studies, 2873 remained after the removal of the duplicated records. Nineteen studies remained after the final selection process. The proportion of emotional symptoms and behavior changes varied from 5.7% to 68.5%; anxiety 17.6% to 43.7%, depression 6.3% to 71.5%, and stress 7% to 25%. Other outcomes such as the prevalence of post-traumatic stress disorder (85.5%) and suicidal ideation (29.7% to 31.3%) were also evaluated. LINKING EVIDENCE TO ACTION: Overall findings showed that the proportion of children and adolescents presenting mental health effects during the COVID-19 pandemic showed a wide variation in different countries. However, there was a trend toward mental health issues. Therefore, policymakers, healthcare planners, youth mental health services, teachers, parents, and researchers need to be prepared to deal with this demand.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".