Pemberian Edukasi dan Pemeriksaan Kesehatan sebagai Deteksi Dini Risiko Penyakit Kardiovaskular
Bibliographic record
Abstract
ABSTRAK Berbagai faktor risiko penyakit kardiovaskular banyak ditemukan oleh masyarakat. Jika faktor risiko tidak segera dideteksi dan diatasi maka dapat berlanjut pada munculnya penyakit kardiovaskular seperti hipertensi, peyakit jantung, dan stroke. Tujuan pengabdian masyarakat untuk meningkatkan pemahaman dan mengidentifikasi faktor risiko penyakit kardiovaskular yang ada di masyarakat. Metode yang dilakukan dengan memberikan edukasi dengan ceramah dan pemeriksaan kesehatan yang meliputi pengukuran lingkar pinggang dan pinggul, tekanan darah, dan kadar gula darah sewaktu. Kegiatan dihadiri oleh 18 orang warga. Hasil diperoleh peningkatan pengetahuan faktor risiko penyebab penyakit kardiovaskular dan cara mencegahnya. Hasil pemeriksaan diketahui faktor risiko yang ditemukan yaitu peningkatan tekanan darah dan RLPP. Kesimpulan penyuluhan dapat meningkatkan pengetahuan faktor risiko dan cara mencegah penyakit kardiovaskular serta pemeriksaan kesehatan dapat dijadikan sebagai deteksi awal penyakit kardiovaskular. Saran untuk pelaksanaan berikutnya agar melakukan pengabdian masyarakat yang bekerja sama dengan para kader agar dapat menjaring masyarakat lebih luas. Kata Kunci: Deteksi Dini, Edukasi, Risiko Penyakit Kardiovaskular ABSTRACT Various risk factors for cardiovascular disease are found by the community. If risk factors are not immediately detected and treated, it can lead to the emergence of cardiovascular diseases such as hypertension, heart disease and stroke. The purpose of community service was to increase understanding and identify risk factors for cardiovascular disease in the community. The method was carried out by providing education with lectures and health checks which include measuring waist and hip circumferences, blood pressure, and blood sugar levels while. The event was attended by 18 residents. The results obtained increased knowledge of risk factors that cause cardiovascular disease and how to prevent it. The results of the examination revealed that the risk factors found were increased blood pressure and RLPP. The conclusion of counseling can increase knowledge of risk factors and how to prevent cardiovascular disease and health checks can be used as early detection of cardiovascular disease. Suggestions for the next implementation are to carry out community service in collaboration with cadres so that they can capture a wider community. Keywords: Early Detection, Education, Risk of Cardiovascular Disease
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 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".