Background: Decentralization is implemented at the local level to increase community participation in improving service delivery. Majority of developing countries are implementing Fiscal decentralization in primary healthcare through various approaches such as Direct Health Facility Financing, among other things, to empower Community governance structures to govern Primary Health Facility operations to improve the responsiveness of health service delivery and achieve Universal Health Coverage. One of the primary functions of these governance committees is to oversee health workers in their health facilities.
Aims: This aimed at assessing how empowered governance committees govern health workers in their facilities under fiscal decentralization.
Methods: To collect data for this study, an explanatory qualitative design with phenomenology traditions was used. To select the area of study, health facilities, and participants, a purposeful sampling procedure was used. Data were gathered through interviews and Focus Group Discussions to explore committee participation in governing health workers in primary care. Thematic analysis was used to analyze the collected data.
Result: The findings of the study suggest that community governance committees' participation in governing health workers under fiscal decentralization remains limited. Majority of the committees have found to have low limited participation in governing different aspects of health workers. The majority of the committees have discovered that hiring casual workers such as security guards and cleaners is more important than other functions.
Conclusion: The study implies that lower and middle-income countries' willingness to implement fiscal reforms at the local level and empower communities to take the lead in governing health workers still there are very limited specific powers granted to them to govern health workers. Therefore, capacity building to the governance actors is critical if we are to achieve the benefit of fiscal decentralization.
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http://dx.doi.org/10.1002/hsr2.1866 | DOI Listing |
Sci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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December 2024
Faculty of Nursing, Chulalongkorn University, Borommaratchachonnani Srisataphat, Building, Rama 1 Road, Pathumwan, 10330, Bangkok, Thailand.
Frontline health workers face a significant issue concerning mental health, particularly stress and burnout. Nurses, being among them, grapple with this problem. The study aims to investigate the prevalence and determinants of burnout among nurses.
View Article and Find Full Text PDFAdv Sci (Weinh)
December 2024
Innovation Center for Diagnostics and Treatment of Thalassemia, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Despite the well-documented mutation spectra of β-thalassemia, the genetic variants and haplotypes of globin gene clusters modulating its clinical heterogeneity remain incompletely illustrated. Here, a targeted long-read sequencing (T-LRS) is demonstrated to capture 20 genes/loci in 1,020 β-thalassemia patients. This panel permits not only identification of thalassemia mutations at 100% of sensitivity and specificity, but also detection of rare structural variants (SVs) and single nucleotide variants (SNVs) in modifier genes/loci.
View Article and Find Full Text PDFJ Community Psychol
January 2025
Nursing Faculty, Public Health Nursing Department, Atatürk University, Yakutiye Erzurum, Turkey.
This study aimed to investigate the resilience, stress levels, coping styles, and the impact of related factors among nurses working in primary healthcare during the COVID-19 pandemic. Designed as a cross-sectional study, the research included 86 volunteer nurses employed in primary healthcare institutions in Bitlis provincial center and its districts in Turkey. Data were collected between March and June 2022 using a sociodemographic information form, the Resilience Scale for Adults, and the Ways of Coping Questionnaire.
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