Social Media Use, Social Connectedness, and Physical Distancing Among University Students During the COVID-19 Pandemic
Bibliographic record
Abstract
The overall purpose of this cross-sectional, survey-based study was to examine university students’ social media use, perceptions of in-person and online social connectedness, and feelings about physical distancing during the early phases of the COVID-19 pandemic in Ontario, Canada. University students’ (N = 1,588; Mage = 22.4, SD = 5.1; 80.6% female) survey responses revealed high levels of in-person (Mitem = 4.4, SD = 0.8) and online (Mitem = 3.8, SD = 0.7) connectedness. Students who reported greater perceptions of connectedness were those whose social media use: (a) had “increased greatly” since the start of the pandemic; and (b) was active (versus passive). Connectedness was significantly higher among users of Instagram, Snapchat, and TikTok (versus non-users). Students reporting greater support and attitudes about physical distancing also reported significantly higher connectedness scores. Results are discussed in the context of existing literature and as a basis for potential implications and future directions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".