Factors on Students Social Media Addiction and its Outcomes Based on Multiple Linear Regression Model
Bibliographic record
Abstract
Social media becomes increasingly important for students for entertainment, learning and social activities. However, more students are diagnosed as addicted to social media, influencing their daily life. Hence, this paper analyzes a cross-national data set of students from high school or university. By applying multiple linear regression model, this study explored the relationship between eleven different factors and students’ addiction level, trying to find out what were the major causes of students’ social media addiction (SMA) and whether it would have detrimental effects on students’ daily life, including academic performance and mental health. To ensure the accuracy of data analysis, collinearity diagnosis is applied. In conclusion, Age, Academic Level and Average Daily Use Hours are the major causes of SMA. Students with a younger age or lower educational level have a higher risk of addicted to social media. Longer usage hours or higher frequency also alleviates the tendency. SMA would detrimentally affect students’ sleep quality and academic performance, and it also leads to mental illness. However, there was no evident proof that gender or relationship status is related with addiction.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".