School and learning contexts during the COVID-19 pandemic: Implications for child and youth mental health
Bibliographic record
Abstract
Abstract Despite significant disruption to school during the COVID-19 pandemic, research on the impact on children is sparse. This study examines in-person and virtual learning contexts and the impact of school format on mental health (MH). Children and adolescents were recruited from community and clinical settings. Parents and children completed prospective online surveys about school experiences (November 2020) and MH symptoms (February/March 2021), including school format and activities. Standardized measures of depression, anxiety, inattention, and hyperactivity were collected. Hierarchical regression analyses tested associations between school format and MH. Children (N = 1011; aged 6–18 years) attending school in-person (n = 549) engaged in high levels of participation in COVID-19 health measures and low levels of social learning activities. Learning online in high school was associated with greater MH symptoms (B = -2.22, CI[-4.32,-.12] to B = -8.18, CI[-15.59,-.77]). Children with no previous MH condition that attended school virtually experienced a similar magnitude of MH symptoms as those with previous MH conditions. However, children who attended school in a hybrid in-person format, with no previous MH condition, experienced less hyperactivity as same-age peers with prior MH problems (B = -8.08, CI[1.58,14.58]). Children’s learning environments looked very different compared to before the pandemic. Removing children from school environments and limiting opportunities that support their MH, such as social learning activities, is problematic. Efforts to address the learning contexts to protect the mental health of children are needed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".