Comparative assessment of health-related quality of life among hypertensive patients attending state and federal government teaching hospitals in Ekiti State, Nigeria
Bibliographic record
Abstract
Hypertension is a serious health problem and it is one of the diseases that impair health-related quality of life. The central tenet of care should be to improve health-related quality of life and overall well-being and not just be limited to improving clinical outcomes. This study assesses and compares health-related quality of life and its predictors among hypertensive patients in two government hospitals in Ekiti State, Nigeria. This was a comparative cross-sectional study involving 440 hypertensive patients (220 in each group), recruited using a systematic sampling technique within the hospitals. Data on socio-demographic, economic and clinical characteristics including the cost of care for hypertension were collected from the patients. The WHOQoL-BREF questionnaire was used to assess health-related quality of life. Data were entered and analyzed using IBM SPSS Statistics for Windows, Version 22.0. All domains of health-related quality of life were better among patients in federal government teaching hospitals, however, only the physical (T = −7.932, p < 0.001) and overall (T = −2.783, p = 0.006) domains were of statistical significance. An inverse relationship between cost and health-related quality of life was found in the two hospitals (State: r = −0.224, p = 0.001; Federal: r = −0.378, p < 0.001). Identified predictors of health-related quality of life were age, locality of residence, income, number of complications, exercise and smoking in both hospitals. Other predictors were marital status, living arrangement, occupation, number of medications, and involvement in religious and spiritual activities among patients in the state government teaching hospital; household size, length of diagnosis, and indirect cost among patients in the federal government teaching hospital. There is a need to support hypertensive patients in the state government teaching hospitals to reduce the inequality of low health-related quality of life among them. Identified predictors should be taken into consideration when putting in place policies that will improve the health-related quality of life of these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".