Child Technology Use during the COVID-19 Pandemic: A Longitudinal Study
Bibliographic record
Abstract
During the COVID-19 pandemic, government-mandated lockdowns led to a rise in technology use, and they also significantly impacted children’s social interactions. Technology use can be categorized as process-oriented (i.e., using technology for non-social purposes) or social-oriented (i.e., using technology to communicate with others). As part of a larger investigation on children’s mental health during the pandemic, this study investigates how children in Southwestern Ontario, ages 8-13, used technology during the pandemic and its impact on mental health and social support. Reports from 178 caregiver and 147 children, assessing demographics, virtual school attendance, child technology use, social support, family stress, and mental health, were collected monthly from June 2020 to January 2021 and again in March 2021. Fluctuations in technology use, particularly computer use, were observed. Children who attended school virtually for majority of the study period reported engaging in greater amounts of technology use than those who attended virtual schooling less often. Children who reported lower friend social support engaged in higher levels of technology use across time; however, they engaged in social-oriented technology use less than other technologies. TV, internet, video game, and computer use was greater for children who reported lower friend social support. Additionally, perceived social support, particularly family support, predicted lower levels of anxiety, depression, and PTSD symptomatology, while social media use predicted higher levels of these internalizing symptoms. Overall, the findings suggest that technology use was multifaceted across the early pandemic. Children appeared to engage in greater amounts of distraction-based technology use, which may have been a helpful strategy for coping with stress of an uncontrollable event, such as a global pandemic. On the other hand, social-oriented technology use did not appear to have strong effects across the early pandemic. Keywords: COVID-19, children, technology use, social support, virtual school, mental health.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".