Monthly correlates of longitudinal child mental health during the COVID-19 pandemic according to children and caregivers
Bibliographic record
Abstract
Background. Multiple reviews identify the broad, pervasive initial impact of the global COVID-19 pandemic on the mental health of children, who may be particularly vulnerable to long-term psychiatric sequelae of the ongoing pandemic. However, limited longitudinal research examines persistence of, or change in, children’s distress or psychiatric symptomatology.Methods. From June 2020 through December 2021, we enrolled two cohorts of families of children aged 8 to 13 from Southwestern Ontario into a staggered baseline, longitudinal design that leveraged multi-informant report (N=317 families). In each family, one child and one parent or guardian completed a baseline assessment, 6 monthly follow-up assessments, and one final follow-up assessment 9 months post-baseline. At each assessment, the child and parent or guardian completed the CoRonavIruS health Impact Survey and measures of child anxiety, depressive, irritability, and posttraumatic stress syndromes.Results. Results indicate a broad impact of the pandemic on children’s mental health, which fluctuated over the study period. Elevated local monthly COVID-19 prevalence, hospitalization, and death rates were associated with monthly elevations in children’s reported worry about contracting COVID-19 and stress related to non-pharmaceutical interventions (NPI). In turn, both elevated monthly worry about contracting COVID-19 and NPI-related stress were associated with monthly elevations in child- and parent- or guardian-report of children’s psychological distress and psychiatric syndromes.Conclusions. This study illustrates the importance of, and informs the potential design of, longitudinal research to track the broad, sustained impact of the COVID-19 pandemic on the mental health of children, who may be particularly vulnerable during the ongoing global crisis.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".