INCREASING THE CAPACITY OF CADRE FOR CONTROLLING HIGH BLOOD PRESSURE (HYPERTENSION) IN THE COMMUNITY IN THE WORKING AREA OF CIBADAK HEALTH CENTER RANGKASBITUNG LEBAK DISTRICT
Bibliographic record
Abstract
57 million (63%) mortality occurs in the world, and 36 million (43%) morbidity is caused by non-communicable diseases. WHO in 2010 reported that 60% of the causes of death for all ages in the world were PTM and 4% died before the age of 70 years. Community service in the regional partnership program contributed to the Cibadak Rangkasbitung Community Health Center program with the Lebak District Health Office, PTM patient care with hypertension control. Efforts by the Health Office, Cibadak Health Center to educate the community on prevention, control through POSBINDU, training of cadres in the village on hypertension control. not yet optimal and evenly reach the community. The solution is to increase the understanding of cadres in the village of the Cibadak Community Health Center who have not had training in controlling high blood pressure (hypertension). The method of carrying out the Pre test, providing information inviting health service resource persons to increase the cadre's understanding of the concept of hypertension, prevention efforts, early detection. Lecturers train the skills of cadres to measure blood pressure with digital blood pressure. The next meeting was Post test, Monev cadres conducted education, examined 5 patients with high risk of hypertension. There was an increase in the average score of community knowledge about hypertension from 64.3% to 88.3% from 64.3%, there was an increase in the skills of cadres to measure blood pressure with digital blood pressure from 5 people to 12 people. As a continuation of activities, there needs to be community participation, good cooperation between health cadres and village supervisors, program holders
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".