Scrolling Through the COVID-19 Pandemic: Exploring the Perceived Effects of Increased Social Media Use on the Mental Health of Undergraduate University Students
Bibliographic record
Abstract
Social media has become increasingly integrated into the lives of students for the past decade; however, the public health restrictions associated with the COVID-19 pandemic have led to a sharp increase in social media use in a short period of time. The purpose of this study was to investigate the effects of social media use on university students during the COVID-19 pandemic. Fifteen students from a mid-sized Canadian city were interviewed to share their experiences with social media during the COVID-19 pandemic. Purposive sampling was conducted to gather a diverse sample of participants, including individuals of various ages, gender and sexual identities, and ethnicities. Thematic analysis on the 15 interviews was completed using NVivo (version 12). Participants experienced both advantages and disadvantages associated with social media use. Ease of communication and stress relief were acknowledged as the strongest benefits. Social comparison, loneliness, development of bad habits, and lack of focus were cited as major disadvantages to social media use during the pandemic. Cost-benefit analysis of social media was common, and participants expressed the importance of using social media with moderation, balance, and awareness. Our study indicates that the focus on health with respect to the pandemic should not be solely based on physical health, rather the potential mental health risks associated with social media use during the pandemic should be recognized and addressed by healthcare providers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".