Institutional Residence Protects Against Cognitive Frailty: A Cross-Sectional Study
Bibliographic record
Abstract
Based on the complex aging background, more and more older people have to live in an institution in later life in China. The prevalence of cognitive frailty (CF) is more higher in institutions than in communities. Rarely studies were conducted on the relationship between institutional residence and CF. Hence, this study were performed to determine the relationship between institutional residence (living in a nursing home) and CF in older adults. A total of 1004 older community residents and 111 older nursing home residents over 50 years of age from Hefei, Anhui Province, China were recruited. CF included physical frailty (PF) and mild cognitive impairment (MCI). PF was assessed using the Chinese version of the Fried frailty scale, MCI was assessed using the Montreal Cognitive Assessment, and the common associated factors including sedentary behavior, exercise, intellectual activity, comorbidity, medication, chronic pain, sleep disorders, nutritional status and loneliness were analyzed using regression logistic models. Multivariate regression logistic analysis showed that exercise (P = .019, odds ratio [OR] = 0.494, 95% confidence interval [CI]: 0.274-0.891), intellectual activity (P = .019, OR = 0.595, 95% CI: 0.380-0.932), medication use (P = .003, OR = 2.388, 95% CI: 1.339-4.258), chronic pain (P = .003, OR = 1.580, 95% CI: 1.013-2.465) and loneliness (P = .000, OR = 2.991, 95% CI: 1.728-5.175) were significantly associated with CF in community residents; however, only sedentary behavior (P = .013, OR = 3.851, 95% CI: 1.328-11.170) was significantly associated with CF in nursing home residents. Our findings suggest that nursing homes can effectively address many common risk factors for CF, including lack of exercise and intellectual activity, medication use, chronic pain, and loneliness, better than the community setting. Thus, residing in a nursing home is conducive to the intervention of CF.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".