Social capital interventions targeting older people and their impact on health: a systematic review
Bibliographic record
Abstract
BACKGROUND: Observational studies show that social capital is a protective health factor. Therefore, we aim to assess the currently unclear health impact of social capital interventions targeting older adults. METHODS: We conducted a systematic review based on a logic model. Studies published between January 1980 and July 2015 were retrieved from MEDLINE, EMBASE, CINAHL, PsycINFO, Cochrane Central Register of Controlled Trials and Web of Science. We included randomised controlled trials targeting participants over 60 years old and focused on social capital or its components (eg, social support and social participation). The comparison group should not promote social capital. We assessed risk of bias and impact on health outcomes and use of health-related resources applying a procedure from the Canadian Agency for Drugs and Technologies in Health (CADTH) based on vote-counting and standardised decision rules. The review protocol was registered in PROSPERO (reference number CRD42014015362). RESULTS: We examined 17 341 abstracts and included 73 papers reporting 36 trials. Trials were clinically and methodologically diverse and reported positive effects in different contexts, populations and interventions across multiple subjective and objective measures. According to sufficiently reported outcomes, social capital interventions showed mixed effects on quality of life, well-being and self-perceived health and were generally ineffective on loneliness, mood and mortality. Eight trials with high quality showed favourable impacts on overall, mental and physical health, mortality and use of health-related resources. CONCLUSIONS: Our review highlights the lack of evidence and the diversity among trials, while supporting the potential of social capital interventions to reach comprehensive health effects 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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".