Loneliness among older adults in the community during COVID-19: a cross-sectional survey in Canada
Bibliographic record
Abstract
OBJECTIVE: Physical distancing and stay-at-home measures implemented to slow transmission of novel coronavirus disease (COVID-19) may intensify feelings of loneliness in older adults, especially those living alone. Our aim was to characterise the extent of loneliness during the first wave in a sample of older adults living in the community and assess characteristics associated with loneliness. DESIGN: Online cross-sectional survey between 6 May and 19 May 2020. SETTING: Ontario, Canada. PARTICIPANTS: Convenience sample of members of a national retired educators' organisation. PRIMARY OUTCOME MEASURES: Self-reported loneliness, including differences between women and men. RESULTS: 4879 respondents (71.0% women; 67.4% 65-79 years) reported that in the preceding week, 43.1% felt lonely at least some of the time, including 8.3% who felt lonely always or often. Women had increased odds of loneliness compared with men, whether living alone (adjusted OR (aOR) 1.52, 95% CI 1.13 to 2.04) or with others (2.44, 95% CI 2.04 to 2.92). Increasing age group decreased the odds of loneliness (aOR 0.69 (95% CI 0.59 to 0.81) 65-79 years and 0.50 (95% CI 0.39 to 0.65) 80+ years compared with <65 years). Living alone was associated with loneliness, with a greater association in men (aOR 4.26, 95% CI 3.15 to 5.76) than women (aOR 2.65, 95% CI 2.26 to 3.11). Other factors associated with loneliness included: fair or poor health (aOR 1.93, 95% CI 1.54 to 2.41), being a caregiver (aOR 1.18, 95% CI 1.02 to 1.37), receiving care (aOR 1.47, 95% CI 1.19 to 1.81), high concern for the pandemic (aOR 1.55, 95% CI 1.31 to 1.84), not experiencing positive effects of pandemic distancing measures (aOR 1.94, 95% CI 1.62 to 2.32) and changes to daily routine (aOR 2.81, 95% CI 1.96 to 4.03). CONCLUSIONS: While many older adults reported feeling lonely during COVID-19, several characteristics-such as being female and living alone-increased the odds of loneliness. These characteristics may help identify priorities for targeting interventions to reduce loneliness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".