The course of children's mental health symptoms during and beyond the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic is associated with increases in child mental health problems, but the persistence of these changes in the post-pandemic era remains uncertain. Additionally, it is unclear whether changes in mental health problems during the pandemic exceed the anticipated increases as children age. This study controls for the linear effect of age in 1399 children, investigating the course of child-reported anxiety, depression, hyperactivity, and inattention symptoms during and after the pandemic, and identifies risk and protective factors that predict these mental health trajectories. METHODS: Children (51% male; ages 9-11 at the first timepoint) provided mental health ratings at three pandemic timepoints (July-August 2020; March-April 2021; November 2021-January 2022) and one post-pandemic timepoint (January-July 2023). Mothers reported pre-pandemic mental health (2017-2019) and socio-demographic factors. Children reported socio-demographic factors, risk (e.g. screen time, sleep), and resilience (e.g. optimism) factors during the first timepoint. RESULTS: Average mental health symptoms increased over time, with more children exceeding clinical cut-offs for poor mental health at each subsequent pandemic timepoint. Growth curve modeling, adjusting for age-related effects, revealed a curvilinear course of mental health symptoms across all domains. Examination of risk and protective factors revealed that pre-existing mental health symptoms and optimism were associated with the course of symptoms. CONCLUSIONS: After considering age effects, children's mental health follows a curvilinear pattern over time, suggesting an initial decline followed by a rising trend in symptoms post-COVID. These findings underscore the continued need for additional resources and timely, evidence-based mental health prevention and intervention for children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".