Investigation and analysis of depression and cognitive status of elderly people in aged care institutions in urban area of Hangzhou
Bibliographic record
Abstract
Objective To investigate the status quo of depression and cognitive function of the elderly in aged care institutions in the urban area of Hangzhou, analyze the influencing factors of cognitive function and its correlation with depression, so as to provide reference for the design of later intervention measures. Methods A total of 130 elderly people from 3 aged care institution of 3 districts of Hangzhou were recruited by convenience sampling method from March to May 2018. Montreal Cognitive Assessment and the Chinese version of Geriatric Depression Scale were applied to assess the cognitive function and depression symptoms of the elderly in aged care institutions. Logistic regression analysis was used to explore the influencing factors of cognitive function, and Pearson correlation analysis was applied to analyze the correlation between depression and cognitive function in elderly people in aged care institutions. Results The incidence of depression in the elderly was 24.62% (32/130) and the incidence of mild cognitive impairment was 89.23% (116/130) . The results of Logistic regression analysis showed that gender (OR=5.379, 95%CI: 1.152-25.109) , age (OR=1.129, 95%CI: 1.008-1.266) and educational level (OR=0.353, 95%CI: 0.193-0.645) entered the model. Pearson correlation analysis showed that there was no significant correlation between depression level and cognitive function in the elderly in aged care institutions (P>0.05) . Conclusions Depression and cognitive status of the elderly people in aged care institutions are in urgent need of attention. It is suggested that an intervention that focuses on reducing the incidence of depression and cognitive impairment should be provided to improve the quality of life of the elderly people. Key words: Aged; Depression; Cognitive function; Aged care institutions
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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 | 0.000 | 0.001 |
| 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".