Effects of social support interventions on depressive symptoms and quality of life among older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Interventions that include participation in social or group connections as measures to prevent or reduce depression have received little attention. This systematic review and meta-analysis aimed to determine the effects of social support interventions on depressive symptoms and Quality of life (QoL) among older adults. Methods A detailed search of six databases comprising Medline, CINAHL, PubMed, Cochrane Library, African Journals Online and Web of Science Core Collections was conducted until January 2025. A review protocol was developed and registered with the PROSPERO database (ID-CRD42021283342). A meta-analysis was used to synthesize the findings of the included studies based on subgroups of social support interventions. Of the 1524 articles found from the six databases, only 16 randomised controlled trials (14 parallel and 2 cluster) were eligible for inclusion. Conclusion Social support interventions included emotional support, social engagement, instrumental, instrumental and appraisal, and social engagement and appraisal support. Meta-analysis findings indicated that social support interventions had non-significant effects on depression and QoL among older adults. Social support interventions have the potential to reduce depressive symptoms and improve QoL. However, current evidence is insufficient to determine the impact of social support interventions on depression and QoL in older adults.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".