Mental health in Canadian children and adolescents during COVID-19 pandemic: the role of personality and, coping and stress responses
Bibliographic record
Abstract
In December 2019, the Coronavirus Disease (COVID-19) pandemic first emerged in China and quickly spread to other countries. Previous studies have shown that the COVID-19 pandemic and the consequences have negatively impacted the mental health of adults. Individual differences such as personality could contribute to mental health. Furthermore, coping and responses to stress may affect an individual's response to the pandemic. In the past, studies have only investigated this relationship in adults. In the current study, we examine how personality traits (using the Five-Factor Model as our framework) and Coping and Response to COVID-19 stress are related to the mental health of Canadian children and adolescents during the pandemic. Using parent reports of 100 preschoolers and 607 6-18-year-old children, we performed multiple regression analysis to explore how personality traits predict the effects of COVID-19 on mental health. The results showed that personality traits are associated with the mental health of Canadian youth during the COVID-19 pandemic. In preschoolers, Neuroticism and Agreeableness predicted the most mental health problems, and in 6-18-year-old children, Extraversion negatively predicted the most mental health problems. Also, Openness to Experience was the weakest predictor of mental health status in Canadian youth. These findings could be useful in understanding children's responses to the COVID-19 pandemic and could assist public health services delivering mental health services specifically tailored to children's personalities during and after this pandemic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".