The Gender Differences in the Relationships Between Self-Esteem and Life Satisfaction with Social Media Addiction Among University Students
Bibliographic record
Abstract
Recent evidence indicates an elevated risk of social media addiction among university students. This research was designed to enhance the understanding of social media addiction among university students by investigating the relationships between self-esteem, life satisfaction, and social media addiction, with the possibility of gender differences in the relationships. 288 university students (103 males, 185 females) from the Faculty of Educational Studies at Universiti Putra Malaysia (UPM) done the Social Media Addiction, Rosenberg Self-Esteem (RSES), and Life Satisfaction Scales. Results showed that self-esteem and life satisfaction accounted for 64% of the total variance in social media addiction. Life satisfaction was a significant factor in increasing the possibility of social media addiction. On the contrary, there were no significant differences in life satisfaction and self-esteem, the latter exhibited no association with social media addiction. Furthermore, males were much more addicted to social media than females. An understanding on gender differences may be helpful for clinicians to expand suitable therapy by taking into account these findings, meanwhile, the statistically significant differences between the variables may contribute to predict student addiction levels in social media. The results of this study are obtained from Malaysian university students and possible generalisation to other populations should be verified by further studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".