Prevalence of Mild-Cognitive-Impairment and Depression among Elderly Clients in Selected Wards of Baraha Municipality: A Cross-Sectional Study
Bibliographic record
Abstract
Introduction: Mild-Cognitive-Impairment (MCI) is an intermediate state between normal cognitive ageing and dementia. Identification of MCI is thought to be crucial to early intervention. Objectives: To assess Mild-Cognitive-Impairment and Depression among elderly clients and to find out the associations between Mild-Cognitive-Impairment and Depression and socio-demographic variables. Methods: A descriptive, cross-sectional study design was adopted. A total of 115 elderly clients who fulfilled the selection criteria were enrolled using purposive sampling technique. Montreal Cognitive Assessment (MoCA) and Geriatric Depression Scale were used with Interview technique. Those elderly clients were selected from the 2 wards of Baraha Municipality. Results: Majority of the subjects (68.7%) were from between 60-70 years. More of the elderly clients were females (54.8%). Maximum (81.7%) were illiterate. About 35.7% had the history of medical and psychiatric illness. From the total sample, 39.1% had issues with memory. In case of Montreal Cognitive Assessment (MoCA), 93.0% was screened with Mild-Cognitive-Impairment. And, 60.9%of the elderly clients were screened as Depression. With regard to the associations between mild cognitive impairment and selected demographic variables, significant associations were found with gender, education level, marital status and previous history of medical/ psychiatric history. The correlation between MoCA score and geriatric depression score showed a negative relationship. Conclusion: Many elderly people in a community have Mild-Cognitive-Impairment and Depression. This study added evidence on prevalence of Mild-Cognitive-Impairment and Depression among geriatric clients in a community-dwelling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".