Children's Mental and Behavioral Health, Schooling, Sociodemographic and Socioeconomic Characteristics During School Closure in France Due To COVID-19: The SAPRIS Project.
Bibliographic record
Abstract
Abstract Background: COVID-19 limitation strategies have led to widespread school closures around the world. The present study reports children’s mental health and associated factors during the COVID-19 school closure in France in the spring of 2020. Methods: We conducted a cross-sectional analysis using data from the SAPRIS project set up during the COVID-19 pandemic in France. Using multinomial logistic regression models, we estimated associations between children’s mental health, children’s health behaviors, schooling, and sociodemographic and socioeconomic characteristics of the children’s families. Results: The sample consisted of 5702 children aged 8 to 9 years, including 50.2% girls. In multivariate logistic regression models, children’s sleeping difficulties were associated with children’s abnormal hyperactivity-inattention (adjusted Odds Ratio (aOR) 2.05; 95% Confidence Interval 1.70-2.47) and emotional symptoms (aOR 5.34; 95% CI 4.16-6.86). Factors specifically associated with abnormal hyperactivity/inattention were: male sex (aOR 2.29; 95% CI 1.90-2.76), access to specialized care prior to the pandemic and its suspension during school closure (aOR 1.51; 95% CI 1.21-1.88), abnormal emotional symptoms (aOR 4.06; 95% CI 3.11-5.29), being unschooled or schooled with assistance before lockdown (aOR 2.13; 95% CI 1.43-3.17), and tutoring with difficulties or absence of a tutor (aOR 3.25; 95% CI 2.64-3.99; aOR 2.47; 95% CI 1.48-4.11, respectively). Factors associated with children’s emotional symptoms were the following: being born pre-term (aOR 1.34; 95% CI 1.03-1.73), COVID-19 cases among household members (aOR 1.72; 95% CI 1.08-2.73), abnormal symptoms of hyperactivity/inattention (aOR 4.18; 95% CI 3.27-5.34) and modest income (aOR 1.45; 95% CI 1.07-1.96; aOR 1.36; 95% CI 1.01-1.84). Conclusions: Multiple characteristics were associated with elevated levels of symptoms of hyperactivity-inattention and emotional symptoms in children during the period of school closure due to COVID-19. Further studies are needed to help policymakers to balance the pros and cons of closing schools, taking into consideration the educational and psychological consequences for 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".