Improving the Degree of Health in the Elderly Through Health Checks and Education
Bibliographic record
Abstract
Background. The thesis focuses on the elderly as the final stage of human development and acknowledges that individuals in old age undergo various changes. The research is conducted at the Nuriah Nursing Home, located in Kecamatan Sei Lakam, Kabupaten Karimun. The purpose of the research is to identify the main health problems faced by the elderly in the nursing home, specifically the risk of declining health status. The thesis mentions specific health problems prevalent among the elderly, such as chronic kidney disease (CKD), high blood pressure, heart disease, rheumatism, paralysis, and diabetes mellitus. Purpose. This activity aims to determine the health status. Method. The method used is by conducting health checks and counseling followed by 5 elderly people.. Results. The results of this activity showed that from 5 elderly people, 1 elderly had hypertension because the elderly underwent dialysis 2 times a week. 1 elderly suffers from malnutrition. 1 elderly had dementia, and one elderly experienced long bed rest. Conclusion. . The conclusion is that 70% are all carried out / assisted by orphanage officers. Starting from bathing, clothes, chapters, and tubs. The elderly seem to understand how to improve their health status. instructional approaches to meet the specific needs of their students.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".