COVID-19 school closures and social isolation in children and youth: prioritizing relationships in education
Bibliographic record
Abstract
We pursue an evidence-informed argument that interpersonal relationships in childhood and adolescence are central to achieving learning outcomes and that school closures across various parts of Canada during the COVID-19 pandemic have compromised these critical relationships, jeopardizing educational attainment. We highlight how the centrality of relationships with peers and educators in achieving learning goals is well established in the literature. So too is the importance of peers in creating stable mental health and wellness for children and youth. The pandemic context has drastically interfered with ongoing wellness, exacerbating feelings of loneliness and social isolation, which takes a toll on what children and youth can achieve in the virtual classroom. In the interest of reducing harm, we call on provincial/territorial governments to move quickly to ensure schools are open in the fall and to think carefully and consult effectively before any further closure decisions are made. We understand that safety is paramount and as such offer a framework for planning a safe return where necessary. Now more than ever there is a need to prioritize social–emotional learning opportunities to protect young people from the lasting effects of social isolation and threats to the fundamental need to belong that have been induced or exacerbated by the 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".