Commentary: The impact of Covid‐19 on psychopathology in children and young people worldwide – reflections on <scp>Newlove‐Delgado</scp> et al. (2023)
Bibliographic record
Abstract
In the past 3 years, since the beginning of the Covid-19 pandemic, there has been an impressive flourishing body of publications on the impact of the pandemic and related restrictions on the mental health of children and young people. It was about time for a rigorous quantitative evidence synthesis of this large body of research. Newlove-Delgado et al. (J Child Psychol Psychiatry, 2022) took on this challenge by completing a systematic review with meta-analysis of epidemiological studies on the impact of Covid-19 on psychopathology in children and adolescents, featured in the 2023 Annual Research Review series of the Journal. Overall, this meta-analysis shows that the relationship between mental health and Covid-19 pandemic in children and adolescents is complex and, as such, it ought to be addressed by studies using rigorous methods and advanced analytic strategies. Collectively, as a field, we should and could do better with regards to the scope and quality of the studies in this area.
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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 | 0.010 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.050 | 0.041 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".