Editorial Perspective: COVID‐19‐related publications on young people’s mental health – what have been the key trends so far and what should come next?
Bibliographic record
Abstract
In this Editorial Perspective, we take a systematic look at the overall nature of the Covid-19 related research on mental health in children and young people, to gain insight into the major trends in this area of research and inform future lines of investigation, clinical practices, and policies. By means of state-of-the-art scientometric approaches, we identified 3,692 relevant research outputs, mainly clustering around the following themes: (a) mental health consequences of the Covid-19 pandemic in children and young people; (b) impact of the pandemic on pre-existing psychiatric disorders; (c) family outcomes (i.e., family violence and parental mental health); and (d) link between physical and mental conditions. Only 23% of the retrieved publications reported new data, the remaining ones being reviews, editorials, opinion papers, and other nonempirical reports. The majority of the empirical studies used a cross-sectional design. We suggest that future research efforts should prioritise: (a) longitudinal follow-up of existing cohorts; (b) quasi-experimental studies to gain insight into causal mechanisms underlying pandemic-related psychopathology in children and young people; (c) pragmatic randomised controlled trials (RCTs) to test evidence-based intervention strategies; and (d) evidence-based guidelines for clinicians and policymakers.
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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 | 0.021 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".