How social factors relate to arthritis risk in Chinese older adults: population-based evidence
Bibliographic record
Abstract
Background Arthritis prevalence is rising among older adults globally, including in China, where the aging population is increasing. Social factors, such as social networks, participation, and support, have been associated with inflammation response. However, limited research has explored how these factors affect arthritis in Chinese older adults. This study aimed to examine the association between social factors and arthritis risk using data from the 2017–2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). Methods A total of 15,854 individuals aged 60 and above were included. Arthritis status was assessed through self-reports, and key social factors—social network size, social participation, and social support were measured. Binary logistic regression models were used to analyze associations, adjusting for demographic and health-related variables. Results A larger social network was associated with a lower risk of arthritis (OR = 0.963, p = 0.015). Higher social participation, however, was associated with a higher risk (OR = 1.027, p = 0.007). Emotional support demonstrated a protective effect (OR = 0.963, p = 0.006), while instrumental support showed no significant impact. A significant interaction between social participation and emotional support (p = 0.008) indicated that emotional support mitigates the elevated risk of arthritis associated with high social participation. Conclusion Social factors play a crucial role in arthritis risk among older adults. While broader social networks and emotional support appear beneficial, increased social participation may contribute to higher risk. These findings highlight the need for tailored interventions to promote healthy aging.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".