Analysis of Characteristics of Health Status Based on Assessment of Cognitive Function in the Elderly in Indonesia
Bibliographic record
Abstract
Introduction: Various challenges will arise due to the increase in elderly people yearly. In Indonesia, the effect of the demographic transition can be seen in the low birth and death rates, and the rise in life expectancy is indirectly the way to increase the number of the elderly; from sharing the problems that arise among the elderly, one of the things that need to be concerned is how to maintain cognitive function so that the elderly can maintain their health status. Objectives: to determine the relationship between characteristics and the health status of the elderly based on the results of cognitive function assessment. Methods: This study is a type of observational analytical research using a cross-sectional study design and was carried out in Gowa Regency, South Sulawesi Province, Indonesia, from August to December 2023, involving 64 older adults with the criteria of elderly people who are 60-75 years old, do not have mental disorders and can read and write. Cognitive function uses the Montreal Cognitive Assessment Indonesia version (MoCA-Ina) and is a variable to be analyzed. Results: There was a significant relationship between sex p<0.030, age p<0.027, education level p<0.001, occupation p<0.045, prayer activity p<0.001 with cognitive function, while marital status was not related to cognitive function p>0.193. Conclusion: The characteristics of the elderly in this study are related to health status based on the value of cognitive function in the elderly.
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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".