Lonely and scrolling during the COVID-19 pandemic: understanding the problematic social media use and mental health link among university students
Bibliographic record
Abstract
Introduction: Undergraduate university students experienced many academic and non-academic stressors during the first year of the coronavirus (COVID-19) pandemic, putting them at a greater risk of negative mental health outcomes. Reports worldwide have shown high incidences of depressive, anxiety, and stress scores among university students at the beginning of the pandemic. Emerging evidence also suggests that to cope with the stress and loneliness of the pandemic, many youth and young adults increased the amount of time they spent on social media platforms. Methods: Undergraduate students participated in an online study aimed to understand the link between time spent on social media, coping through the use of social media and problematic social media use (PSMU) with mental health symptoms, such as stress, depression, anxiety, and loneliness, during the COVID-19 pandemic. Results: While time spent on social media was only weakly associated with stress, depression, anxiety and loneliness scores, PSMU more strongly mapped onto these outcomes. Additionally, students who were coping highly using social media displayed elevated stress, depression, anxiety and loneliness levels in comparison to those reporting low levels of coping with social media. Finally, students who reported high levels of coping using social media displayed higher PSMU scores, with this relationship appearing more pronounced in students who had higher levels of loneliness. Conclusion: These data support evidence that it is not necessarily time spent on social media but rather PSMU that is relevant for mental health symptoms, and that PSMU is exacerbated by loneliness. Moreover, the current results highlight the effects of maladaptive coping on mental health symptoms and PSMU among university students during the COVID-19 pandemic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".