Comparative assessment of health-related quality of life among hypertensive patients attending state and federal government teaching hospitals in Ekiti State, Nigeria
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».