SELF-MANAGEMENT AND SEVERITY OF ANGINA PECTORIS AMONG CORONARY HEART DISEASE PATIENT
Bibliographic record
Abstract
The prevalence of coronary heart disease (CHD) in Indonesia continues to increase. Padang ranks 10th with the most CHD patients in Indonesia. The clinical manifestation of CHD is angina pectoris. One of the efforts in minimizing angina pectoris is self- management. This study aimed to determine the relationship between self-management and the degree of angina pectoris in coronary heart disease patients at the cardiac polyclinic of RSUD dr. Rasidin Padang. This study used a quantitative research design with a cross-sectional approach. Sampling in this study used purposive sampling technique with 106 respondents. The instruments used were CSMS (Coronary self-management scale) and Canadian Cardiovascular Society angina grade (CCS). The results of univariate analysis showed that the patient's self-management was in the good range and the highest degree was in the angina II category. The bivariate analysis test using the chi-square correlation test showed a significant relationship between self-management and the degree of angina pectoris with significance 0.000 (? <0.005), meaning that there was a significant relationship between self-management and the degree of angina pectoris and coefficient relation was 0.95 (strong relation). The limitation of this study was the cross-sectional design used where the measurement of the dependent variable and the independent variable was carried out simultaneously so that it cannot prove the existence of cause and effect between the two variables. It is hoped that the hospital through health workers can increase health education in an effort to increase adequate knowledge related to self-management of coronary heart disease patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".