Within- and between-person associations between social interactions and loneliness: students’ experiences during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic introduced many restrictions to in-person interactions, and remote social interactions may be especially important for managing loneliness when such restrictions are in place. However, it is unclear how social interactions are related to loneliness when in-person interactions are limited. Data were collected between February 2021 and March 2022 from a sample of 581 university students. Participants reported their loneliness and participation in positive in-person or remote social interactions each day for 14 days. Results from dynamic structural equation models showed that participants felt less lonely than they usually felt on the days they engaged in positive remote interactions at the within-person level. Moreover, participants generally felt less lonely when engaging more frequently in remote interactions, but only when in-person interactions were restricted (between-person level). Some of these results varied by changing COVID-19 restrictions. Finally, for participants who felt lonelier in general, the effect of positive in-person and remote interactions on loneliness was less strong. These findings suggest that social interactions may buffer loneliness but are not as impactful for those who experience greater loneliness.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".