Socially Distant? Social Network Confidants, Loneliness, and Health during the COVID-19 Pandemic
Bibliographic record
Abstract
In the wake of the COVID-19 pandemic, many nations around the world instituted strict social distancing measures. Although necessary to deter the spread of the virus, these measures may also have had adverse health repercussions by increasing social isolation. Using a national longitudinal study from Canada, in which respondents were surveyed in March 2020 at the beginning of stay-at-home orders and again two months later in May, we show that, at baseline, loneliness was inversely associated with perceptions of self-rated health, and there was a beneficial indirect association between respondents’ number of social network confidants and perceived health through lower levels of loneliness. Between March and May, social network confidants decreased and loneliness increased; these changes were independent of each other and contributed to declines in self-rated health. Greater loneliness at baseline was also associated with declines in self-rated health. Our observations suggest that social distancing during the COVID-19 pandemic impaired social connectedness, thereby resulting in declines in perceptions of health. We conclude by discussing several policy-related implications of our findings.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".