The Implementation of Local Wisdom to Improve the Health and Quality of Life the Hypertension Family as a New Strategy for Early Prevention of Stroke
Bibliographic record
Abstract
Indonesia is well known as a country which has high ethnic diversity. Thus, each ethnic specifically has a local wisdom as their own identity. However, the economic growth, globalization, and modernization have eroded the local wisdom in which it is potentially forgotten by the next generation. The modernization also has been proved changing the behavior, lifestyle, and live quality of society. As the result, Hypertension are documented as the highest case diseases generated by the change of behaviour, lifestyle, and lack quality of life. However, the hypertension is possibly controlled by the intervention of life-style and improving the quality of life. This present study aims to implement the local wisdom of Aceh to improve the quality of life as new strategy to early prevention of stroke disease. The research design using Participatory Action Research including reconnaissance, planning, action, evaluation, and reflection based on Aceh local wisdom are specifically used as a new approach to reach the goal. The result shows that the local wisdom can be used as the main reason to change the society behaviour. The implementation of local wisdom potentially become the solution, because the society, especially the one who have the history of hypertension coming from their family, can empower family health problem by providing high attention to family health problem. Additionally, the local wisdom encourages the society to check their health status as control, whereas most of participant is only visiting the health center when they did not feel good. The result also proved that the local wisdom can initiate the society to modify their own environment, indicating the increase of awareness about the importance of cleaning the environment. The successful implementation of local wisdom should be supported and developed to follow the modernization without losing its identity because the fast growing of globalization cannot be hold but can be well maintained.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".