Screen Time and Online University Student Perceptions of Their Mental and Physical Well-Being
Bibliographic record
Abstract
The increasing prevalence of online university programs has led to heightened screen time among students, raising concerns about its effects on mental and physical well-being. This study examines university students' perceptions of screen time’s impact in an online learning context. A total of 91 students from a Canadian university provided open-ended responses, analyzed using thematic and sentiment analysis. Results indicate that screen time is predominantly perceived negatively, with themes of digital fatigue, social isolation, physical discomfort, and mental strain emerging. Participants reported experiences of anxiety, burnout, and a sedentary lifestyle, with screen exposure contributing to mental exhaustion, reduced motivation, and physical symptoms such as eye strain and back pain. However, a subset of participants noted positive effects, including screen time’s role in facilitating social connections, access to educational resources, and engagement with fitness applications. Sentiment analysis supported these findings, revealing a prevalence of mild to moderate negative sentiment, with frustration and fatigue being commonly expressed. These findings emphasize the need for institutional interventions that support healthy screen time management, such as incorporating digital wellness initiatives, ergonomic guidance, structured screen breaks, and virtual peer engagement opportunities to mitigate the negative effects of prolonged screen use. While this study provides valuable insights, its self-reported, cross-sectional design and limited sample size suggest the necessity of longitudinal and objective research to explore screen time’s long-term impact on student health and to inform policies that promote sustainable digital learning environments.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".