Loneliness during Covid-19: Does Living Situation or Ability to Access Information about Social Activities Matter?
Bibliographic record
Abstract
Abstract Social isolation is deleterious for both mental and physical health (Coyle & Dugan, 2012; Hawkley et al., 2006). Conversely, social participation has mental and physical health benefits (Novek et al., 2013). In light of the current Covid-19 pandemic requiring social distancing, the present study examined whether living situation and ability to access information about social activities are associated with older adults’ loneliness during the pandemic. Specifically, we surveyed ninety-one adults aged 60 years or older in May and June of 2020, at a time when social distancing measures were still in place. We tested whether their living situation and having access to information about social activities was associated with loneliness. OLS regression analyses revealed living alone was associated with higher loneliness (b = .43, p = .050); and having access to information about social activities was associated with lower loneliness (b = -.18, p = .027) amidst the pandemic. The analyses controlled for participants’ age, gender, and education. Our findings highlight that during Covid-19, older adults’ living situation and access to information about social activities matter and may impact their social behavior. Thus, at this difficult time, it is recommended organizations that offer social activities find creative ways to reach those living alone who will benefit most from having access to such activities.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".