Analytical Assessment of Belief about Medicine among Patients with Hypertension: A Case Study on Patients Referred to Medical Centers
Bibliographic record
Abstract
INTRODUCTION & OBJECTIVE: Hypertension (HTN) is one of the major health problems in many countries. Medicinal treatments and lifestyle modification have so far failed to effectively influence blood pressure control. Hence, this study intended to analytically assess the belief about medicine among hypertensive patients who referred to medical centers during 2015. MATERIALS & METHODS: This was a descriptive-analytical, cross-sectional study. The population consisted of all patients who referred to health centers and doctors’ offices for internal medicine and heart diseases. The sample included a total of 400 hypertensive patients who were selected through convenience method sampling and purposeful. Data were collected through a questionnaire related to belief about medicine for hypertensive patients. The findings were analyzed through the Mann-Whitney U test and Kruskal-Wallis test through SPSS version 21.0 FINDINGS: Based on the results, more than half of the subjects believed that the medicines are generally addictive and harmful, and should be taken regularly while the natural and herbal remedies are safer. Furthermore, the majority of patients believed that doctors, who have too much confidence in the medicines, tend to over-prescribe. In fact, there was a significant relationship between certain demographic characteristics of the hypertensive patients and belief about medicines. CONCLUSIONS: Overall, the results suggested there is belief about medicine among seniors unlike most other populations. This can provide an opportunity for nurses, health care administrators, etc. to take improvement measures in the treatment of hypertensive 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".