Impact of COVID-19 Pandemic on Screen Time: Findings From a Cross-Sectional Observational Study Among College Students From India
Bibliographic record
Abstract
In this study, we aimed to assess the impact of the COVID-19 pandemic on the amount and pattern of screen time among college students. The relationship between increased screen time and quality of life (QoL), COVID-related stress, and personality traits were also explored. A cross-sectional online survey-based study was conducted among Indian college students who were recruited by purposive sampling. Details regarding socio-demographics, amount and pattern of screen time usage, change in screen time patterns during the COVID-19 pandemic, and COVID-related stress were collected. In addition, personality traits and QoL were assessed with validated questionnaires. A total of 731 responses (51% female, mean age 20.7 years) were analysed. Of the participants, 93.2% self-reported an increase in daily screen time during COVID-19. The predominant reasons for the increased screen time were educational screen time (89.6%), streaming or watching videos for entertainment (82.8%), use of social media for non-communication purposes (78.1%), communication with friends and/or family members (76.2%), reading or watching news (65.9%), and interactive recreational screen time (44.7%). A small but significant negative correlation between increased screen time and QoL (rs = -0.154, p < .001) was found. Increased screen time due to the use of social media for non-communication purposes was associated with poorer QoL (U = 32947.50; p = .02) and greater COVID stress (U = 32381.50; p = .01). Educational screen time was the most common cause for increased screen time among college students and was not associated with negative effects on QoL. The context and purpose of screen time appears to be important in ascertaining the impact of screen time on QoL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".