Challenges faced by community health nurses to achieve universal health coverage in Myanmar:A mixed methods study

Objective:This study aimed to identify the challenges of community health nurses(CHNs)in delivering effective community health care to achieve universal health coverage(UHC)in Myanmar.Methods:A total of 30 CHNs from township health centers in the northeastern,southern,and western parts of Myanmar were purposefully recruited for quantitative and qualitative interviews.Quantitative data were processed using Microsoft Excel software,and qualitative data were analyzed using thematic analysis.This study is registered with researchregistry6201.Results:Around the country,30 CHNs uncovered their hardships in implementing primary health care to achieve UHC.Over 90%of the participants agreed to the problem of inadequate health infrastructure,while half of them felt unmotivated when they encountered role conflicts among various cadres of healthcare providers and poor opportunities for career promotion.Major problems arose from the lack of standard professional education at the entry point to community settings because most CHNs did not achieve specialized training in providing public health services.Complications are incapable of evaluating health services for policy-making and the inability to conduct health research to develop evidencebased practices.Insecure work and living conditions,unsupportive community relationships,and undereducation in professional practices were supportive major themes explored by CHNs to achieve a deeper understanding of the barriers to UHC.Not only the health system itself but also the population and other geographical factors have contributed to many challenges to CHNs.Conclusion:Myanmar’s CHNs face many challenges in achieving UHC.These challenges are not confined to the health sector.Some situations,such as geographical barriers and transportation problems,remain persistent challenges for healthcare providers.This study highlights the fact that current health systems should be strengthened by qualified healthcare providers and sufficient infrastructure.Meanwhile,public empowerment plays a critical role in promoting health development.

A dynamic logistic regression for network link prediction

In social network analysis, link prediction is a problem of fundamental importance. How to conduct a comprehensive and principled link prediction, by taking various network structure information into consideration,is of great interest. To this end, we propose here a dynamic logistic regression method. Specifically, we assume that one has observed a time series of network structure. Then the proposed model dynamically predicts future links by studying the network structure in the past. To estimate the model, we find that the standard maximum likelihood estimation(MLE) is computationally forbidden. To solve the problem, we introduce a novel conditional maximum likelihood estimation(CMLE) method, which is computationally feasible for large-scale networks. We demonstrate the performance of the proposed method by extensive numerical studies.