Pojok kasih lansia: optimizing the quality of life for older adults in lumajang regency through the development of an elderly-friendly park
Bibliographic record
Abstract
The increasing elderly population in Lumajang Regency, especially in Yosowilangun District, indicates progress in life expectancy but also brings challenges related to elderly care and dependency. To address these issues, a women's empowerment program was conducted in 2023 through the mentoring of Elderly Care Mothers, focusing on promoting elderly independence and caregiver support. In 2024, the initiative continued with the development of a Senior-Friendly Park equipped with ergonomic seating, sensory therapy, and recreational facilities under the concept of the Elderly Care Corner. This program aimed to improve the quality of life of older adults, enhance community involvement, and support caregiving efforts. The activities included park construction, public engagement, capacity-building for elderly care cadres, and the development of educational materials for caregivers. The outcomes demonstrated active participation of 75 elderly individuals within the first two months, a 32 percent increase in knowledge among 20 trained elderly care cadres, and reduced workload reported by 68 percent of caregivers. Improvements were also recorded in quality of life scores, particularly in life satisfaction and daily activities. Qualitative findings highlighted increased social interaction, motivation, and caregiver confidence. This initiative has encouraged replication in other villages and is expected to contribute positively to the well-being of the elderly population in Lumajang Regency.
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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 | 0.000 |
| 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) | 0.003 | 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".