Loneliness among older adults in the community during COVID-19
Bibliographic record
Abstract
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 characterize the extent of loneliness in a sample of older adults living in the community and assess characteristics associated with loneliness. Design Online cross-sectional survey between May 6 and May 19, 2020 Setting Ontario, Canada Participants Convenience sample of the members of a national retired educators’ organization. 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% that felt lonely always or often. Women had increased odds of loneliness compared to men, whether living alone (adjusted Odds Ratio (aOR) 1.52 [95% Confidence Interval (CI) 1.13-2.04]) or with others (2.44 [95% CI 2.04-2.92]). Increasing age group decreased the odds of loneliness (aOR 0.69 [95% CI 0.59-0.81] 65-79 years and 0.50 [95% CI 0.39-0.65] 80+ years compared to <65 years). Living alone was associated with loneliness, with a greater association in men (aOR 4.26 [95% CI 3.15-5.76]) than women (aOR 2.65 [95% CI 2.26-3.11]). Other factors associated with loneliness included: fair or poor health (aOR 1.93 [95% CI 1.54-2.41]), being a caregiver (aOR 1.18 [95% CI 1.02-1.37]), receiving care (aOR 1.47 [95% CI 1.19-1.81]), high concern for the pandemic (aOR 1.55 [95% CI 1.31-1.84]), not experiencing positive effects of pandemic distancing measures (aOR 1.94 [95% CI 1.62-2.32]), and changes to daily routine (aOR 2.81 [95% CI 1.96-4.03]). 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. Strengths and limitations of this study This survey study leveraged a strong community-based partnership to obtain timely data from a large sample of older Canadians on the impacts of COVID-19. This study identified several characteristics that increased the odds of loneliness, which may help to identify priorities for targeted interventions to reduce loneliness. The data were based on a convenience sample of retired, educational staff, who are not fully representative of the Canadian population. The perspectives of vulnerable groups who may be at greater risk for loneliness (e.g. those with severe mental health illness, low income, no home internet access, etc.) are likely underrepresented in this sample.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".