Establishment of a Medical Team in Girimekar Village, Bandung District
Bibliographic record
Abstract
Bhakti Kencana University (BKU) community service activities were conducted in Girimekar Village, Bandung Regency. This activity involved BKU lecturers from various disciplines, such as Midwifery, Nursing, Public Health, and Pharmacy. This activity was carried out by forming a medical team, which was an effort to empower the community, especially the youth of the local group. This activity aims to socialize emergency treatment measures that must be carried out as soon as possible before residents are taken to the community health center or hospital. This will help the community care more about their health. Girimekar Village has topography and land contours in the form of highlands and lowlands at an altitude of between 400 meters above sea level and 600 meters above sea level, with an average temperature ranging from 19°C to 37°C. Girimekar Village consists of 5 hamlets, 22 RWs, and 79 RTs. Based on the topography, most areas outside the forest area are slopes or peaks with varying heights. Most RWs are located outside forest areas. Conditions like this are the background for the formation of this medical team. The method for forming a medical team was carried out by identifying the number of young people in the area and then carrying out preliminary outreach by educating them about the importance of emergency treatment before residents are taken to the hospital. After the socialization, the medical team was given training on first aid in the form of basic life support. The formation of the medical team was carried out by first identifying the number of youth. This activity, which was carried out for one month, produced a medical team from among the youth who were expected to be at the forefront in assisting. First, to residents before taking them to the health center or hospital. 26 Girimekar residents attended it, and the long-term plan for this activity is to hold first aid training for the medical team at least once every six months.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 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) | 0.017 | 0.002 |
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".