Optimalisasi Peran Kader Kesehatan dalam Edukasi dan Implementasi Gaya Hidup Sehat Lansia
Bibliographic record
Abstract
The health problems of the elderly in the Puskesmas Industri, Gresik work area align with the Indonesian health phenomenon where the number of elderly and degenerative diseases is high. Health cadres training in the work area of the Puskesmas Industri is needed to optimize the role of cadres in educating and implementing a healthy lifestyle in older adults. The Community Partnership Program, focusing on health as part of goal 3 of The Sustainable Development Goals (SDGs), was carried out on September 1, 2022, and was attended by 57 cadres from 6 villages. Health information was provided as a provision for cadres to provide education to the public about the healthy lifestyle of the elderly. The results showed that there was an increase of 17,26 points (34,76%) after training. The lowest post-test results were on the topic of BMI and its interpretation, as well as physical activity in the elderly, so further action is needed. During FGD, the Elderly Health Book from the Ministry of Health of the Republic of Indonesia was introduced. The results of the FGD showed that the introduction of the Elderly Health Book should be continued so that health cadres can consistently use it. Health cadres have a significant role in realizing the elderly health program from the Indonesian Ministry of Health. This health cadre training has proven to be very necessary because it can increase understanding as a provision for cadres in providing education to the public regarding the healthy lifestyle of the elderly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".