The impact of COVID-19 on the mental health of Canadian children and youth
Bibliographic record
Abstract
Children and youth flourish in environments that are predictable, safe, and structured. The COVID-19 pandemic has disrupted these protective factors making it difficult for children and youth to adapt and thrive. Pandemic-related school closures, family stress, and trauma have led to increases in mental health problems in some children and youth, an area of health that was already in crisis well before COVID-19 was declared a global pandemic. Because mental health problems early in life are associated with significant impairment across family, social, and academic domains, immediate measures are needed to mitigate the potential for long-term sequalae. Now more than ever, Canada needs a national mental health strategy that is delivered in the context in which children and youth are most easily accessible—schools. This strategy should provide coordinated care across sectors in a stepped care framework and across a full continuum of mental health supports spanning promotion, prevention, early intervention, and treatment. In parallel, we must invest in a comprehensive population-based follow-up of Statistics Canada’s Canadian Health Survey on Children and Youth so that accurate information about how the pandemic is affecting all Canadian children and youth can be obtained. It is time the Canadian government prioritizes the mental health of children and youth in its management of the pandemic and beyond.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".