Disruption to Education During COVID-19: School Nonacademic Factors Are Associated with Children's Mental Health
Bibliographic record
Abstract
OBJECTIVE: Few studies have examined aspects of the school environment, beyond modality, as contributors to child and youth mental health during the coronavirus pandemic. We investigated associations between nonacademic school experiences and children's mental health. METHODS: Parents of children ages 6 to 18 years completed online surveys about school experiences (November 2020) and mental health (February/March 2021). Parent-reported and child-reported school experiences (i.e., nonacademic factors) included school importance, adapting to public health measures, and school connectedness. Children's mental health symptoms of depression, anxiety, inattention, and hyperactivity were collected using standardized parent-reported measures. RESULTS: Children's (N = 1052) self-reported and parent-reported nonacademic factors were associated with mental health outcomes, after adjusting for demographics and previous mental health. Lower importance, worse adapting to school changes, and less school connectedness were associated with greater depressive symptoms ( B = -4.68, CI [-6.04, -3.67] to - 8.73 CI [-11.47, 5.99]). Lower importance and worse adapting were associated with greater anxiety symptoms ( B = - 0.83 , CI [-1.62, -0.04] to -1.04 CI [-1.60, -0.48]). Lower importance was associated with greater inattention (B = -4.75, CI [-6.60, -2.90] to -6.37, CI [-11.08, -7.50]). Lower importance and worse adapting were associated with greater hyperactivity (B = -1.86, CI [-2.96, -0.77] to -4.71, CI [-5.95, -3.01]). CONCLUSION: Schools offer learning opportunities that extend beyond curriculum content and are a primary environment where children and youth develop connections with others. These aspects of school, beyond academics, should be recognized as key correlates of child and youth mental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".