Do Interventions Reducing Social Vulnerability Improve Health in Community Dwelling Older Adults? A Systematic Review
Bibliographic record
Abstract
Jasmine Mah,1 Kenneth Rockwood,2 Susan Stevens,3 Janice Keefe,3 Melissa K Andrew2 1Department of Medicine, Dalhousie University, Halifax, NS, Canada; 2Division of Geriatric Medicine, Dalhousie University, Halifax, NS, Canada; 3Department of Family Studies and Gerontology, Mount Saint Vincent University, Halifax, NS, CanadaCorrespondence: Jasmine Mah, Geriatric Medicine Research, Dalhousie University/Nova Scotia Health, 1315 – 5955 Veterans’ Memorial Lane, Halifax, Nova Scotia, B3H 2E1, Canada, Tel +1-613-618-8810, Email jmah@dal.caBackground: Social vulnerability occurs when individuals have been relatively disadvantaged by the social determinants of health. Complex interventions that reduce social vulnerability have the potential to improve health in older adults but robust evidence is lacking.Objective: To identify, appraise and synthesize evidence on the effectiveness of complex interventions targeting reduction in social vulnerability for improving health related outcomes (mortality, function, cognition, subjective health and healthcare use) in older adults living in the community.Methods: A mixed methods systematic review was conducted. Five databases and targeted grey literature were searched for primary studies of all study types according to predetermined criteria. Data were extracted from each distinct intervention and quality was assessed using the Mixed Methods Appraisal Tool. Effectiveness data were synthesized using vote counting by direction of effect, combining p values and Albatross plots.Results: Across 38 included studies, there were 34 distinct interventions categorized as strengthening social supports and communities, helping older adults and their caregivers navigate health and social services, enhancing neighbourhood and built environments, promoting education and providing economic stability. There was evidence to support positive influences on function, cognition, subjective health, and reduced hospital utilization. The evidence was mixed for non-hospital healthcare utilization and insufficient to determine effect on mortality.Conclusion: Despite high heterogeneity and varying quality of studies, attention to reducing an older adult’s social vulnerability assists in improving older adults’ health.Keywords: social determinants, older adults, complex interventions, social frailty
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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 | 0.020 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".