Browsing by Author "Kibiriti, Hillary"

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    Effect of Socio-demographic Characteristics on Health System Responsiveness in Diabetic and Hypertensive Clinics: A Cross-sectional Study in Tier Three Hospitals in Kenya
    (International Journal of Professional Practice (IJPP), 2024-04-19) Kibiriti, Hillary; Tenambergen, Mwaura Wanja; Mapesa, Job O.
    This study aimed to investigate the effect of socio-demographic characteristics on health system responsiveness within diabetic andhypertensive clinics in tier three hospitals in Kenya. Responsiveness, which refers to meeting non-health-improving expectations, is crucial for a well-functioning health system, and gaps in responsiveness can compromise the quality of healthcare. While both client and health system factors contribute to responsiveness, the specific influence of socio-demographic characteristics on health systems responsiveness remains unexplored in Kenyan chronic care centers.The cross-sectional descriptive survey involved 308 respondents from Kimilili, Uasin Gishu, and Gatundu hospitals. Data were collected using a structured questionnaire that assessed responsiveness domains such as promptness, respect, communication, involvement, confidentiality, choice, cleanliness, social support access, and overall trust, rated on a five-point Likert scale. Socio-demographic factors investigated included facility location, gender, age, medical condition, religion, marital status, education levels, income level, occupation, and insurance enrollment. The mean responsiveness score was 98.8 (63.7%), with only 38.3% of respondents reporting favorable outcomes. Chi-square analysis revealed significant associations (p<0.05) between responsiveness and facility location, religion, marital status, occupation, and medical condition. Age, gender, insurance enrollment, education, and income level showed no significant association (p>0.05) with responsiveness. The study concluded that favorable responsiveness was less likely than unfavorable outcomes, highlighting the significance of socio-demographic factors. It recommends that healthcare managers prioritize holistic, patient-centered interactions to improve responsiveness in chronic care clinics, taking into account the influence of socio-demographic characteristics on patients' experiences and expectations.
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    ModelingPredictors of Health System Responsiveness amongChronic Care Centersin Tier Three Hospitals in Kenya
    (InternationalJournalof ProfessionalPractice (IJPP), 2024-05-03) Kibiriti, Hillary; Tenambergen, Mwaura Wanja; Mapesa, Job O.
    This study sought to model predictors of health system responsiveness among diabetic and hypertensive patients in Kenyan primary hospitals. Responsiveness in the health system hinges on service provision and system demands, but there are noted deficiencies in Kenya prompting this study.Thestudy exploredhow valuations, accountability, access, structural factors, organizational culture, and perceptions of justice impact responsiveness. This cross-sectional survey provided baseline data for an intervention study. Froma sampling frame of853patients,323 were sampled using the Fishers et al. formula. Of these, 308 questionnaires were completed: 130 from Gatundu, 98 from Uasin Gishu, and 80 from Kimilili Hospitals. Data was collected through structured questionnaires using a five-point Likert Scale, after which scores were summed up and divided into favourable and unfavorable using the demarcation threshold formula.Only38.3% of respondents reported favorable responsiveness. Three predictors; accountability, structural and organizational culture had majority in the unfavorable, while valuations, access, and justice had majority in the favorable category. Following conditional backward binomial logistic regression, the final model included four significant predictors of responsiveness; namely,structural, accountability, organizational culture, and justice perceptions. Using the Nagelkerke statistic, the model explained 15.7% variation in responsiveness. The model achieved a 79.5% success rate in predicting unfavorable responsiveness and a 46.6% success rate in predicting favorable responsiveness, with an overall correct prediction rate of 66.9%. The probability of experiencing favorable responsiveness given positive experiences in the predictors was 68.5%. In conclusion, responsiveness remains low. Critical predictors identified in this study serve as intervention targets for improving responsiveness. With 15.7% explained variation in responsiveness, there's room for further model enhancement. The study recommends managers to adopt a holistic, patient-cantered care approach, and suggests implementation studies to validate the model across diverse contexts and identify additional predictive factors for responsiveness improvement.