Longitudinal Problematic Social Media Use in Students and Its Association with Negative Mental Health Outcomes
Bibliographic record
Abstract
Purpose: Social media has become increasingly part of our everyday lives and is influential in shaping the habits, sociability, and mental health of individuals, particularly among students. This study aimed to examine the relationship between changes over time in problematic social media use and mental health outcomes in students. We also investigated whether resilience and loneliness moderated the relationship between social media use and mental health. Patients and Methods: A total of 103 participants completed a baseline virtual study visit, and 78 participants completed a follow-up visit, 4-weeks later. Participants completed a comprehensive set of questionnaires measuring symptoms of depression and anxiety, perceived stress, loneliness, and resilience. Results: Our results showed that problematic social media use at baseline was significantly negatively correlated with resilience and positively correlated with all other mental health outcomes. Furthermore, increases in problematic social media use were significantly associated with increased depressive symptoms and loneliness between visits. Resilience significantly moderated the relationship between increased problematic social media use and heightened perceived stress. Poor mental health at baseline did not predict increased problematic social media use over time. Contrarily to problematic use, frequency of social media use was not significantly correlated with any mental health measures at baseline. Conclusion: This study offers a longitudinal perspective, providing valuable insights into the potential protective role of resilience against the detrimental mental health effects seen with increases in problematic social media use.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".