Hubungan Kepatuhan Minum Obat Terhadap Kualitas Hidup Penyandang Hipertensi Dengan Penyakit Penyerta Dan Tanpa Penyakit Penyerta
Bibliographic record
Abstract
Hypertension is a condition when blood pressure above normal, that systolic >140 mmHg and dyastolic >100 mmHg. Hypertension is also referred as a silent killer disease because it does not generate signs and symptoms. Hypertension is the third leading cause of death in the world, while the main cause of death is hypertension with comorbidities. Comorbidities of hypertension are diabetes, kidney disease, atherosclerosis due to myocardium infarction, angina, heart failure, and stroke. The success of hypertension treatment can be seen from adherence, especially adherence of medication. Good medication adherence can improve the quality of life. Meanwhile, poor medication adherence can make quality of life worse. This study aims to determine the relation of medication adherence to the quality of life hypertension with comorbidities hypertension and without comorbidities hypertension in Baki Health Center area. This type of research is a quantitative research with a correlation description method. Sampling used a purposive sampling technique involving 68 respondents. The characteristics of respondents were hypertension people with and without comorbidities who had an examination in Baki Health Center, were more than 25 years old, were cooperative and agree to become respondents by signing informed consent. In this study, the measuring instrument used questionnaire. The questionnaire used in this study were the Morisky Medication Adherence Scale (MMAS-8) to assess respondents medication adherence and the World Health Organization-BREF (WHOQOL-BREF) to assess respondents quality of life. This study used used the Spearman Test with the result of the study there was a significant relationship between medication adherence to the quality of life of people with and without comorbidities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".