Identifying the Factors Related to Depressive Symptoms Amongst Community-Dwelling Older Adults with Mild Cognitive Impairment
Bibliographic record
Abstract
High-level depressive symptoms have been reported in individuals with mild cognitive impairment (MCI), resulting in increased risk of progression to dementia. However, studies investigating the correlates of depressive symptoms among this population are scarce. This study aimed to investigate the significant socio-demographic, lifestyle-related and disease-related correlates of depressive symptoms among this cohort. Cross-sectional data were obtained from a sample of 154 Chinese community-dwelling older adults with MCI. MCI subjects were screened by the Montreal Cognitive Assessment. Depressive symptoms were measured by the Geriatric Depression Scale. Possible correlates of depressive symptoms in individuals with MCI were explored by multiple linear regressions. The prevalence of depressive symptoms among Chinese older adults with MCI was 31.8%. In multiple regression analysis, poor perceived positive social interaction, small social network, low level of physical activity, poor functional status, subjective memory complaint, and poor health perception were correlated with depressive symptoms. The findings highlight that depressive symptoms are sufficient to warrant evaluation and management in older adults with MCI. Addressing social isolation, assisting this vulnerable group in functional and physical activities, and cultivating a positive perception towards cognitive and physical health are highly prioritized treatment targets among individuals with MCI.
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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 | 0.000 |
| 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 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".