Annual Research Review: The impact of Covid‐19 on psychopathology in children and young people worldwide: systematic review of studies with pre‐ and within‐pandemic data
Bibliographic record
Abstract
BACKGROUND: The high volume and pace of research has posed challenges to researchers, policymakers and practitioners wanting to understand the overall impact of the pandemic on children and young people's mental health. We aimed to search for and review the evidence from epidemiological studies to answer the question: how has mental health changed in the general population of children and young people? METHODS: Four databases (Medline, CINAHL, EMBASE and PsychINFO) were searched in October 2021, with searches updated in February 2022. We aimed to identify studies of children or adolescents with a mean age of 18 years or younger at baseline, that reported change on a validated mental health measure from prepandemic to during the pandemic. Abstracts and full texts were double-screened against inclusion criteria and quality assessed using a risk of bias tool. Studies were narratively synthesised, and meta-analyses were performed where studies were sufficiently similar. RESULTS: 6917 records were identified, and 51 studies included in the review. Only four studies had a rating of high quality. Studies were highly diverse in terms of design, setting, timing in relation to the pandemic, population, length of follow-up and choice of measure. Methodological heterogeneity limited the potential to conduct meta-analyses across studies. Whilst the evidence suggested a slight deterioration on some measures, overall, the findings were mixed, with no clear pattern emerging. CONCLUSIONS: Our findings highlight the need for a more harmonised approach to research in this field. Despite the sometimes-inconsistent results of our included studies, the evidence supports existing concerns about the impact of Covid-19 on children's mental health and on services for this group, given that even small changes can have a significant impact on provision at population level. Children and young people must be prioritised in pandemic recovery, and explicitly considered in planning for any future pandemic response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".