Mental Health and Wellbeing of 9–12-year-old Children in Northern Canada Before the COVID-19 Pandemic and After the First Lockdown
Bibliographic record
Abstract
Objectives: Children’s mental health and wellbeing declined during the first COVID-19 lockdown (Spring 2020), particularly among those from disadvantaged settings. We compared mental health and wellbeing of school-aged children observed pre-pandemic in 2018 and after the first lockdown was lifted and schools reopened in Fall 2020. Methods: In 2018, we surveyed 476 grade 4–6 students (9–12 years old) from 11 schools in socioeconomically disadvantaged communities in Northern Canada that participate in a school-based health promotion program targeting healthy lifestyle behaviours and mental wellbeing. In November-December 2020, we surveyed 467 grade 4–6 students in the same schools. The 12 questions in the mental health and wellbeing domain were grouped based on correlation and examined using multivariable logistic regression. Results: There were no notable changes pre-pandemic vs. post-lockdown in responses to each of the 12 questions or any of the sub-groupings. Conclusion: Supporting schools to implement health promotion programs may help mitigate the impact of the pandemic on children’s mental health and wellbeing. The findings align with recent calls for schools to remain open as long as possible during the pandemic response.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".