COVID-19 Stress in Relation to Parent Emotional Intelligence and Child Mental Health among a Canadian Sample
Bibliographic record
Abstract
Due to the unprecedented nature of COVID-19, more studies are needed to examine how parents and children are impacted by the pandemic, and more specifically the role of parental Emotional Intelligence (EI) in the link between COVID-19 stressors and child mental health outcomes. This cross-sectional study investigated the relationships between COVID-19 stressors, parental EI, and child anxiety and depression outcomes. Fifty parents (mean age = 41.98 years; 88% mothers) of children between the ages of 8-11 years old (mean age = 9.46 years; 74% boys) completed online questionnaires assessing COVID-19 stress, parental EI, and child anxiety and depression symptoms. Although no significant results were found between parent COVID-19 stress, parent EI, and child depression symptoms, the results suggest that parental COVID-19 stress was related to child anxiety. Exploratory analyses were conducted examining specific domains of COVID-19 stress, parental EI, and child anxiety and depression symptoms. Findings indicate the resilience of child mood and parental EI to COVID-19 stress among this sample, as well as child anxiety being a potential area of risk during the pandemic. Knowledge of these associations gives insight into areas to prioritize for mental health clinicians in assessment and intervention. Public significance statement: There is a need for more research on parent and child psychological outcomes during COVID-19. This study suggests an association specifically between parental COVID-19 stress and child anxiety, and not with child depressive symptoms. Findings help with better understanding the experiences of families during COVID-19.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".