COVID-19 and Student Well-being: Stress and Mental Health during Return-to-School
Bibliographic record
Abstract
Students have been multiply impacted by the COVID-19 pandemic: threats to their own and their family’s health, the closure of schools, and pivoting to online learning in March 2020, a long summer of physical distancing, and then the challenge of returning to school in fall 2020. As damaging as the physical health effects of a global pandemic are, much has been speculated about the “second wave” of mental health crises, particularly for school-aged children and adolescents. Yet, few studies have asked students about their experiences during the pandemic. The present study engaged with over two thousand (N = 2,310; 1288 female; Mage = 14.5) 12-18-year-old Alberta students during their first few weeks of return-to-school in fall 2020. Students completed an online survey that asked about their perceptions of COVID-19, their fall return-to-school experiences (84.9% returned in-person), their self-reported pandemic-related stress, and their behaviour, affect, and cognitive functioning in the first few weeks of September. The majority of students (84.9%) returned to school in person. Students reported moderate and equal concern for their health, family confinement, and maintaining social contact. Student stress levels were also above critical thresholds for 25 percent of the sample, and females and older adolescents (age 15-18 years) generally reported higher stress indicators as compared to males and younger (age 12-14 years) adolescents. Multivariate analysis showed that stress indicators were positively and significantly correlated with self-reported behavioural concerns (i.e., conduct problems, negative affect, and cognitive/inattention), and that stress arousal (e.g., sleep problems, hypervigilance) accounted for significant variance in behavioural concerns. Results are discussed in the context of how schools can provide both universal responses to students during COVID-19 knowing that most students are coping well, while some may require more targeted strategies to address stress arousal and heightened negative affect.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".