Influence of COVID-19 on social media usage: association with mental well-being in undergraduate students
Bibliographic record
Abstract
Objective During the COVID-19 pandemic, social media usage was rapidly accelerated by increased social isolation due to public health measures that attempted to limit the spread of the virus. Even though numerous studies indicate that increased screen usage during the pandemic is associated with negative mental health outcomes, there is no consensus on the effect of social media apps on mental health. This study investigated the impact of COVID-19 on the mental health of students attending a privately funded Christian university on their social media usage. Methods The data used for this study were collected using a cross-sectional survey involving 36.5% of the full-time undergraduates in the traditional Art Business and Science (ABS) undergraduate program at a privately funded Canadian Christian university. Findings Consistent with previous research, an increase in social media use and its detrimental impact on people’s mental health during COVID-19 that we identified indicates that social media greatly impacts undergraduates’ lives. COVID-19 aggravated social media usage in student groups with a lower mental health status. Nevertheless, moderate social media use on specific social platforms can promote mental health against social isolation and stress generated by the pandemic. In the post-COVID era, the residual pandemic impact was higher for participants with low mental health status. Furthermore, during the pandemic, religion appeared to be a protective factor against excessive screen use on social media. Originality This study examined the use of social media in a Christian university context and highlighted the effect of religion on the screen time of social media among students. To the best of our knowledge, this is the first such study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".