Internet and Smartphone Use, Internet-Related Risks, and Satisfaction with Life Among University Students During the COVID-19 Pandemic: A Cross-Sectional Study
Bibliographic record
Abstract
The purpose of this cross-sectional, survey-based study was to examine university students’ internet and smartphone use (including smartphone habits), internet- and social media-related risks, and satisfaction with life during the early stages of the COVID-19 pandemic in Ontario, Canada. Four objectives were examined, including associations among the study variables, including demographic factors. Results revealed a significant increase in university students’ (N = 1,625; Mage = 22.4, SD = 5.1; 78.8% female) internet use from pre- to early-pandemic (p < .001). Approximately one in ten students reported experiencing internet-related incidents (e.g., bullying, harassment, etc.) during the pandemic. Smartphone use and habits were significantly (p < .001) positively correlated with social media risks, while smartphone use and social media risks were significantly (p < .001) negatively related to students’ satisfaction with life. Results are discussed in the context of existing literature in this area, and potential implications and future directions are presented.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".