Social isolation and loneliness among older adults living in rural areas during the COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
BACKGROUND: The causes and consequences of social isolation and loneliness of older people living in rural contexts during the COVID-19 pandemic were systematically reviewed to describe patterns, causes and consequences. METHODS: Using the Arksey and O'Malley (2005) scoping review method, searches were conducted between March and December 2022, 1013 articles were screened and 29 were identified for data extraction. RESULTS: Findings were summarized using thematic analysis separated into four major themes: prevalence of social isolation and loneliness; rural-only research; comparative urban-rural research; and technological and other interventions. Core factors for each of these themes describe the experiences of older people during the COVID-19 pandemic and related lockdowns. We observed that there are interrelationships and some contradictory findings among the themes. CONCLUSIONS: Social isolation and loneliness are associated with a wide variety of health problems and challenges, highlighting the need for further research. This scoping review systematically identified several important insights into existing knowledge from the experiences of older people living in rural areas during the COVID-19 pandemic, while pointing to pressing knowledge and policy gaps that can be addressed in future research.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".