Prevalence of loneliness and social isolation among older adults during the COVID-19 pandemic: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Pandemics and their public health control measures have generally substantially increased the level of loneliness and social isolation in the general population. Because of the circumstances of aging, older adults are more likely to experience social isolation and loneliness during pandemics. However, no systematic review has been conducted or published on the prevalence of loneliness and/or social isolation among the older population. This systematic review and meta-analysis aims to provide up-to-date pooled estimates of the prevalence of social isolation and loneliness among older adults during the COVID-19 pandemic and other pandemics in the last two decades. DESIGN: EMBASE, PsychoINFO, Medline, and Web of Science were searched for relevant studies from January 1, 2000 to November 31, 2021 published in a variety of languages. Only studies conducted during the COVID-19 pandemic were selected in the review. RESULTS: A total of 30 studies including 28,050 participants met the inclusion criteria. Overall, the pooled period prevalence of loneliness among older adults was 28.6% (95% CI: 22.9-35.0%) and 31.2% for social isolation (95% CI: 20.2-44.9%). Prevalence estimates were significantly higher for those studies conducted post 3-month from the start of the COVID-19 pandemic compared to those conducted within the first 3 months of the pandemic. CONCLUSIONS: This review identifies the need for good quality longitudinal studies to examine the long-term impact of pandemics on loneliness and social isolation among older populations. Health policymaking and healthcare systems should proactively address the rising demand for appropriate psychological services among older adults.
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 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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".