Physical frailty and its associated factors among elderly nursing home residents in China
Bibliographic record
Abstract
BACKGROUND: Evidence is scarce on the trend in prevalence of physical frailty in China; the primary purpose of this study was to identify the prevalence and correlates of physical frailty among older nursing home residents in China. METHODS: Cross-sectional study in 20 nursing homes in Changsha, China. Physical frailty was defined based on the frailty phenotype including weight loss, low grip strength, exhaustion, slow gait speed, and low physical activity. Participants with at least three affected criteria were defined as being frail. Participants with one or two affected criteria were considered as pre-frail, and those with no affected criteria were considered as robust. A total of 1004 nursing home residents aged 60 and over were included in this study. A multinomial logistic regression model was used to analyze the associations of physical frailty with its potential risk factors, including age, sex, education levels, marital status, type of institution, living status, current drinking, current smoking, regular exercise, and self-reported health. RESULTS: The overall prevalence of physical frailty and prefrailty was 55.6, and 38.5%, respectively. The rate of physical frailty substantially increased with age, and was higher in women than in men (69.5% vs. 30.5%). The multinomial logistic regression analysis showed that older age, being women, living in a private institution, living alone or with unknown person, having no regular exercise (≤ 2 times/week), and poor self-reported health were significantly associated with increased odds of being physically frail. CONCLUSION: We demonstrated physical frailty is highly prevalent among older residents in nursing homes in China, especially in women. The potential role of those associated factors of physical frailty warrant further investigations to explore their clinical application among elderly nursing home residents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".