Prevalence of Cognitive Impairment and its Associated Factors among Institutionalized Senior Citizens: A Cross-sectional Study
Bibliographic record
Abstract
A BSTRACT Background: The global elderly population is increasing steadily. The impact of this increasing elderly population on the healthcare system of low- and middle-income countries is huge. Cognitive impairment (CI) is a common geriatric healthcare problem that needs to be tackled urgently. The present study aims to assess the prevalence and associated factors of CI among institutionalized senior citizens. Materials and Methods: A nonexperimental research design with a descriptive cross-sectional survey approach was used. Using a purposive sampling technique, five old-age homes in Ernakulam district were selected for the study. Formal permission for the study was obtained from the institutional ethics committee and old-age home authorities. A total of 236 senior citizens participated in the study. Informed consent was taken from individual participants. Socio-personal data and cognition of senior citizens were collected using socio-personal data sheet and Montreal Cognitive Assessment, respectively, through interviews. The collected data were analyzed using descriptive and inferential statistics. Results: The average age of participants was 72.76 ± 9.17. Only 9% of participants had normal cognition. Most participants either had mild cognitive decline (45%) or moderate cognitive decline (42%). The mean cognitive score was found to be 16.59 ± 5.12. Educated senior citizens having phone contact with relatives and participating in leisure activities for more than 15 min a day were found to be at lower risk of developing CI. Conclusion: The current study points to the alarming situation of senior citizens in Kerala. Yet the study imparts hope that through lifestyle modifications (education, leisure activities, and frequent contact with relatives), we can reduce the prevalence of CI among senior citizens.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".