Loneliness Progression Among Older Adults During the Early Phase of the COVID-19 Pandemic in the United States and Canada
Bibliographic record
Abstract
OBJECTIVES: Older adults are at high risk for complications from coronavirus disease 2019 (COVID-19). Health guidelines recommend limiting physical contact during the pandemic, drastically reducing opportunities for in-person social exchange. Older adults are also susceptible to negative consequences from loneliness, and the COVID-19 pandemic has likely exacerbated this age-related vulnerability. METHODS: In 107 community-dwelling older individuals (65-90 years, 70.5% female) from Florida, the United States, and Ontario, Canada, we examined change in loneliness over the course of the pandemic after implementation of COVID-19-related physical distancing guidelines (March-September 2020; T1-T5; biweekly concurrent self-report) using multilevel modeling. We also explored gender differences in loneliness during the early phase of the COVID-19 pandemic at both data collection sites. RESULTS: Consistent across the 2 sites, levels of loneliness remained stable over time for the full sample (T1-T5). However, our exploratory moderation analysis suggested gender differences in the trajectory of loneliness between the United States and Canada, in that older men in Florida and older women in Ontario reported an increase in loneliness over time. DISCUSSION: Leveraging a longitudinal, binational data set collected during the early phase of the COVID-19 pandemic, this study advances understanding of stability and change in loneliness among a North American sample of individuals aged 65 and older faced with the unique challenges of social isolation. These results can inform public health policy in anticipation of future pandemics and highlight the need for targeted intervention to address acute loneliness among older populations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".