Relationship between Multimorbidity and Cognitive Decline Moderated by Social Health
Bibliographic record
Abstract
Background and Objectives: Multimorbidity is one of the important problems in health that can lead to cognitive decline. There is notable literature revealing that multimorbidity and cognitive ability are associated with social health and social-related activity. In this regard, this study aimed to investigate the role of social health in the relationship between multimorbidity and cognitive decline. Materials and Methods: This descriptive-correlational study was conducted on all elderly people aged 70 years and above referring to the outpatient clinics of hospitals in Tehran, Iran, within July-September 2019. The volunteer samples (n=270) were selected from three randomly selected hospitals, namely Imam Khomeini, Sina, and Shariati, using the availability sampling method. The instruments of the study included questionnaires, namely a social-demographic form, the Chronic Diseases Checklist, Montreal Cognitive Assessment (MoCA) Test, and Social Health Questionnaire. The collected data were analyzed in SPSS software (version 22) using linear regression. Results: The results showed that MoCA (cognitive decline) had a significant relationship with multimorbidity (β=0.58, 95% CI: 0.62-0.54, P<0.001) and social health (β=-0.21, 95% CI: -0.26. -0.16, P<0.001). Moreover, the findings indicated that social health was a mediator variable between multimorbidity and cognitive decline (β=0.12, 95% CI: 0.09-0.14, P<0.001), in which the amplification of social health would modulate the negative effect of multimorbidity on cognition ability. Conclusion: According to the results of the present study, social health was a moderating variable in the relationship between multimorbidity and cognitive decline. In the other words, social health was a protective factor against a particular risk factor, such as multimorbidity, in protecting cognitive abilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".