Quality of life and well-being during the COVID-19 pandemic: associations with loneliness and social isolation in a cross-sectional, online survey of 2,207 community-dwelling older Canadians
Bibliographic record
Abstract
BACKGROUND: The far-reaching health and social sequelae of the COVID-19 pandemic among older adults have the potential to negatively impact both quality of life (QoL) and well-being, in part because of increased risks of loneliness and social isolation. The aim of this study was to examine predictors of QoL and well-being among Canadian older adults within the context of the pandemic, including loneliness and social isolation. METHODS: This cross-sectional, online survey recruited older adult participants through community organizations and research participant panels. Measures included the: Older People's Quality of Life Scale-B, WHO-5, DeJong Gierveld Loneliness Scale, Lubben Social Network Scale and five COVID-19 specific items assessing impact on loneliness and social isolation. Multiple linear regression models were used to adjust for potential confounders. RESULTS: A total of 2,207 older Canadians (55.7% female, with a mean age of 69.4 years) responded to the survey. Over one-third strongly disagreed that the pandemic had had a significant effect on either their mental (35.0%) or physical health (37.6%). Different patterns of predictors were apparent for QoL and well-being. After adjusting for all variables in the models, the ability of income to meet needs emerged as the strongest predictor of higher QoL, but was not associated with well-being, except for those who chose not to disclose their income adequacy. Age was not associated with either QoL or well-being. Females were more likely to experience lower well-being (β=-2.0, 95% C.I. =-4.0,-0.03), but not QoL. Reporting three or more chronic health conditions and that the COVID-19 pandemic had a negative impact on mental health was associated with lower QoL and well-being. Loneliness was a predictor of reduced QoL (β=-1.4, 95% C.I. =--1.6, -1.2) and poor well-being (β=-3.7, 95% C.I. =-4.3,-3.0). A weak association was noted between QoL and social isolation. CONCLUSIONS: The COVID-19 pandemic is associated with differential effects among older adults. In particular, those with limited financial resources and those with multiple chronic conditions may be at more risk to suffer adverse QoL and well-being consequences. Loneliness may be a modifiable risk factor for decreased QoL and well-being amenable to targeted interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".