Contributions of Social Support to Mitigate the Impact of the COVID-19 Pandemic on Pediatric Depressive and Irritability Symptoms
Bibliographic record
Abstract
Prior research, including my initial research on the mental health of children inSouthwestern Ontario, highlighted the broad, widespread impact of the COVID-19 pandemic on the mental health of adults, children, and youth, globally, including the potential for social support to attenuate the harmful impact of the pandemic. Social support, one’s belief that others will help in times of need, may protect against the impact of myriad life stressors on the development of psychopathology. The present study examines the potential for social support to mitigate the longitudinal impact of the COVID-19 pandemic on children’s irritability and depressive symptoms. Families (N = 317) encompassing one child aged 8 to 13 and a parent or guardian reported on children’s perception of social support, irritability, and depressive symptoms at baseline assessment and seven follow-up assessments over nine months between June 2020 and December 2021. Although depressive and irritability symptoms fluctuated over time, children’s initial perception of social support availability from family and friends did not predict interindividual variability in irritability or depressive symptom change over time. Social support fluctuated over time but showed no systematic increase or decrease. At each monthly assessment, social support was associated with child- and parent- or guardian-report of children’s symptomatology; children self-reported, and parents or guardians observed, lower symptomatology on months when children reported higher social support. Findings highlight the importance of social support for pediatric depression and irritability and suggest that social support may be bolstered during public health crises to assist children.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".