Status quo and influencing factors of frailty of community⁃dwelling elderly people
Bibliographic record
Abstract
ObjectiveTo investigate the current situation of frailty of community⁃dwelling elderly people,and to analyze its influencing factors.MethodsFrom March to June 2024,a stratified sampling method was adopted to select 762 elderly people living at home in communities from 6 cities in China as the survey subjects.The subjects were investigated by using a self⁃designed questionnaire and the Edmonton Frailty Scale(EFS).ResultsThe overall incidence of frailty of community⁃dwelling elderly people was 50.0%,among which it was 45.2% in northeast China,25.8% in north China,78.1% in central China,50.1% in east China,and 53.3% in south China.The age,polypharmacy,occupation,self⁃care ability,hospitalization due to illness within one year,self⁃perceived health status,dietary habits,monthly medication expenses,self⁃medication management methods,use of sedatives,use of anti⁃infection drugs,and regular physical examinations were the influencing factors of frailty in the elderly(P<0.05).ConclusionsThe overall incidence of frailty of community⁃dwelling elderly people was relatively high.Community workers should implement interventions based on the influencing factors of frailty of community⁃dwelling elderly people,so as to reduce their incidence of frailty.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".