ModelingPredictors of Health System Responsiveness amongChronic Care Centersin Tier Three Hospitals in Kenya

Simple item page
dc.contributor.authorKibiriti, Hillary
dc.contributor.authorTenambergen, Mwaura Wanja
dc.contributor.authorMapesa, Job O.
dc.date.accessioned2024-11-12T07:48:23Z
dc.date.available2024-11-12T07:48:23Z
dc.date.issued2024-05-03
dc.description.abstractThis 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.
dc.identifier.citationKibiriti, H., Tenambergen, W. M., & Mapesa, J. (2024). Modeling Predictors of Health System Responsiveness among Chronic Care Centers in Tier Three Hospitals in Kenya. International Journal of Professional Practice, 12(3), 1–14. https://doi.org/10.1234/ijpp.v12i3.321
dc.identifier.urihttps://repository.ru.ac.ke/handle/123456789/1430
dc.language.isoen
dc.publisherInternationalJournalof ProfessionalPractice (IJPP)
dc.relation.ispartofseriesVol .12 Issue No. 3,
dc.subjectResponsiveness
dc.subjectChronic conditions
dc.subjectDiabetes Mellitus
dc.subjectHypertension
dc.subjectPrimary hospital
dc.titleModelingPredictors of Health System Responsiveness amongChronic Care Centersin Tier Three Hospitals in Kenya
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Kibiriti,+Tenambergen+and+Mapesa.......pdf
Size:
328.2 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download

Collections

RU Community