The association of social networks and depression in community-dwelling older adults: a systematic review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Depression is a globally prevalent mental condition, particularly among older adults. Previous research has identified that social networks have a buffering effect on depression. Existing systematic reviews have either limited their research to specific geographic areas or provided evidence from over a decade ago. The vast body of recent literature particularly from the last decade emphasizes the need for a comprehensive review. This systematic review aims to analyze the association of structural aspects of social networks and depression in older adults. METHODS: The electronic databases APA PsycINFO, ProQuest, PSYINDEX, PubMed, Scopus, SocINDEX, and Web of Science were searched from date of data base inception until 11 July 2023. Studies were eligible for inclusion if they reported on community-dwelling older adults (defined as a mean age of at least 60 years old), had an acceptable definition for depression, referred to the term social network in the abstract, and were published in English. Quality was appraised using the Newcastle Ottawa Scale for cross-sectional and longitudinal studies. Outcome data were extracted independently from each study and analyzed by direction of the relationship, social network domain and cross-sectional or longitudinal study design. RESULTS: In total, 127 studies were included. The study categorizes structural network aspects into seven domains and finds that larger and more diverse networks, along with closer social ties, help mitigate depression. The literature on the relationships between depression and network density, homogeneity, and geographical proximity is scarce and inconclusive. DISCUSSION AND IMPLICATIONS: Despite inconsistent findings, this review highlights the importance of quantifying complex social relations of older adults. Limitations of this review include publication and language bias as well as the exclusion of qualitative research. Further research should use longitudinal approaches to further investigate the reciprocal relationship between social networks and depression. Following this review, interventions should promote the integration of older adults in larger and more diverse social settings. Other: This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant [454899704]. This systematic review was pre-registered. The review-protocol can be accessed at https://doi.org/10.17605/OSF.IO/6QDPK .
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".