Social isolation, loneliness and positive mental health among older adults in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Social isolation and loneliness are associated with poorer mental health among older adults. However, less is known about how these experiences are independently associated with positive mental health (PMH) during the COVID-19 pandemic. METHODS: We analyzed data from the 2020 and 2021 cycles of the Survey on COVID-19 and Mental Health to provide estimates of social isolation (i.e. living alone), loneliness and PMH outcomes (i.e. high self-rated mental health, high community belonging, mean life satisfaction) in the overall older adult population (i.e. 65+ years) and across sociodemographic groups. We also conducted logistic and linear regressions to separately and simultaneously examine how social isolation and loneliness are associated with PMH. RESULTS: Nearly 3 in 10 older adults reported living alone, and over a third reported feelings of loneliness due to the pandemic. When examined separately, living alone and loneliness were each associated with lower PMH. When assessed simultaneously, loneliness remained a significant independent factor associated with all three PMH outcomes (overall and across all sociodemographic groups), but living alone was only a significant factor for high community belonging in the overall population, for males and for those aged 65 to 74 years. CONCLUSION: Overall, social isolation and loneliness were associated with poorer wellbeing among older adults in Canada during the pandemic. Loneliness remained a significant factor related to all PMH outcomes after adjusting for social isolation, but not vice versa. The findings highlight the need to appropriately identify and support lonely older adults during (and beyond) the pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".