Children's Mental Health in Southwestern Ontario during Summer 2020 of the COVID-19 Pandemic.
Bibliographic record
Abstract
OBJECTIVE: COVID-19 presents an unprecedented global crisis. Research is critically needed to identify the impact of the pandemic on children's mental health including psychosocial factors that predict resilience, recovery, and persistent distress. The present study collected data in June-July 2020 to describe children's mental health during the initial phase of the pandemic, including the magnitude and nature of psychiatric and psychological distress in children, and to evaluate social support as a putative psychosocial moderator of children's increased distress. METHOD: Children and parents from 190 families of children aged 8 to 13 from the Windsor-Essex region of Southwestern Ontario reported (i) retrospectively on children's well-being (e.g., worry, happiness) immediately prior to the pandemic and (ii) on children's current well-being; irritability; social support; and anxiety, depressive, and posttraumatic stress symptoms at the baseline assessment of an ongoing longitudinal study of the COVID-19 pandemic. RESULTS: Children and parents reported worsened well-being and psychological distress during the pandemic compared to retrospective report of pre-pandemic well-being. Child-perceived social support from family and friends was associated with lower symptom severity and attenuated increase in psychological distress. CONCLUSIONS: Study findings suggest possible broad psychological impacts of the COVID-19 pandemic and are consistent with prior research that indicates a protective role of social support to mitigate the negative psychological impact of the pandemic. These findings may inform clinical assessments and highlight the need for public resources to safeguard children's mental health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".