Effects of Social Media Use on Health and Academic Performance Among Students at the University of Sharjah
Bibliographic record
Abstract
From the statistics, almost 5 billion people in 2020 will be connected to Social Media (SM). Studies have drawn attention to the harms of SM to the health of students; it affects their attention span, memory, sleep, vision, and overall physical, mental, and social health. In this paper, we investigate the effects of SM use on the health and academic performance of students at the University of Sharjah. This study shows that students with more self-regulation have better control over social media use. A cross-sectional mixed approach (CSMA) was used to conduct the research using both quantitative and qualitative data. Out of 300 student participants in our study, the majority of them used Instagram, followed by WhatsApp and Twitter. Students reported an average time of 3-4 hours per day on social media; however, qualitative data showed that many students spent all day on social media. A majority of the students used social media to chat with friends and make new connections. They agreed that their use of social media has reduced reading of paper-based resources and has affected their grammar and writing skills. The use of SM delayed their bedtime and left fewer hours for sleep and caused eyestrain, neck/shoulder pain, fatigue, and poor posture, with declining physical activity. This study concludes that social media use does affect academic performance and health among the students of the University of Sharjah. Considering the negative consequences of extensive social media use, universities need to create awareness programs and can incorporate this as a topic in health education and awareness courses. Our study also generated new information and insights about the effects of high levels of SM usage on the health and academic performance among university students, thereby creating opportunities for further research.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".