Social isolation and loneliness prevention among rural older adults aging-in-place: a needs assessment
Bibliographic record
Abstract
Introduction The adverse effects of social isolation and loneliness (SI/L) have been documented among older adults in rural communities and contribute to poor health outcomes, premature disability and mortality, and increased burden on the healthcare system. The identification of factors contributing to SI/L among older adults can build the foundation for rural policymakers and leaders to allocate resources and develop tailored strategies more efficiently. The purpose of this article is to describe findings from a needs assessment designed to understand local factors that contribute to SI/L among rural older adults in a county in Northeast Tennessee. Findings from the needs assessment will be used by local stakeholders to develop strategies to promote age-friendly initiatives. Methods Eighty-two older adults [ages 62 to 74 years (59%); non-Hispanic white (95%); female (71%)] from three senior apartment complexes in a Northeast Tennessee county completed an 87-item needs assessment survey. The evaluation of social isolation utilized Lubben’s 6-item Social Network Scale, while loneliness was assessed using the 3-item UCLA Loneliness Scale. Logistic regression analysis was used to identify predictors of SI/L. Given the limited sample size, statistical significance was considered at p < 0.10. Results The prevalence of social isolation and loneliness was 42% and 37%, respectively. Residing in the county <5 years [Adjusted OR (AOR): 3.35; 95% CI: 1.04–10.81; p = 0.04] and reporting resource-related barriers to aging-in-place (AOR: 6.56; 95% CI: 2.00–21.57; p = 0.004) were associated with increases in the odds of social isolation; whereas interest in intergenerational activities decreased the odds of social isolation (AOR: 0.19; 95% CI: 0.05–0.69; p = 0.01). Boredom (AOR: 4.06; 95% CI: 1.63–12.11; p = 0.01) and limited knowledge about community services (AOR: 4.61; 95% CI: 1.42–15.02; p = 0.01) quadrupled the odds of loneliness. Similarly, older adults who were frail (AOR: 2.69; 95% CI: 0.88–8.17; p = 0.08) and who rated their community livability as low (AOR: 3.35; 95% CI: 0.81–13.87; p = 0.09) were more likely to experience loneliness. Discussion This needs assessment provided important information about the individual and social drivers of SI/L among rural older adults in the community. Findings support the generation of localized data to support muti-partner efforts to design sustainable programs to address SI/L.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".