Education-Related COVID-19 Difficulties and Stressors during the COVID-19 Pandemic among a Community Sample of Older Adolescents and Young Adults in Canada
Bibliographic record
Abstract
The COVID-19 pandemic created significant disruptions to the provision of education, including restrictions to in-person and remote learning. Little is known about how older adolescents and young adults experienced these disruptions. To address this gap, data were drawn from the Well-Being and Experiences study (the WE Study), a longitudinal community-based sample collected in Manitoba, Canada, from 2017–2021 (n = 494). Prevalent difficulties or stressors during in-person learning were less interaction with friends or classmates, worrying about grades, less interaction with teachers, and too much screen time (range: 47.3% to 61.25%). Prevalent difficulties or stressors for remote learning were less interaction with friends or classmates and teachers, less physical activity, worrying about grades, and too much screen time (range: 62.8% to 79.6%). Differences related to sex, education level, financial burden, and mental health prior to the pandemic were noted. From a public health perspective, efforts to re-establish social connections with friends, classmates, and teachers; strategies to manage stress related to worrying about grades or resources to improve grades that have declined; and approaches to reduce screen time in school and at home may be important for recovery and for any ongoing or future pandemics or endemics that impact the delivery of education.
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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 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".