The multi-tiered medical education system and its influence on the health care market - a China’s flexner report

Background: Medical education is critical and the first step to foster the competence of a physician. Unlike developed countries, China has been adopting a system of multi tiered medical education to training physicians which is featured by the provision of an alternative lower level of medical practitioners , or known as a feldsher system since 1950s This study aimed to illustrate the impact of multi tiered medical education on both the equity in the delivery of health care services and the efficiency of the health care market. Methods Based on both theoretical reasoning and empirical analysis, this paper documented evidences upon those impacts of the medical education system. Results First, the geographic distribution of physicians in China is not uniform across physicians with different education training. Second, we also find the evidence that high educated doctors are more likely to be hired by larger hospitals, which in turn add the fuel to foster the hospital center health care system in China as pati ents choose large hospitals to chase good doctors. Third, through the channels of adverse selection and moral hazard, the heterogeneity in medical education also imposes costs to the health care market in China. Discussion Overall, the three tiered medical education system in China is a standard policy trade off between quantity and quality in training health care professionals. On one hand, China gains the benefit of increasing supply of health care professionals at lower costs. On the other hand, C hina pays the price for keeping a multi tiered medical education in terms of increasing inequality and efficiency loss in the health care sector. Finally, we discuss the potential policy options for China to mitigate the negative impact of keeping a multi tiered medical education on the performance of health care market

Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering

Visual Question Answering (VQA) has emerged as one of the most challenging tasks in artificial intelligence due to its multi-modal nature. However, most existing VQA methods are incapable of handling Knowledge-based Visual Question Answering (KB-VQA), which requires external knowledge beyond visible contents to answer questions about a given image. To address this issue, we propose a novel framework that endows the model with capabilities of answering more general questions, and achieves a better exploitation of external knowledge through generating Multiple Clues for Reasoning with Memory Neural Networks (MCR-MemNN). Specifically, a well-defined detector is adopted to predict image-question related relation phrases, each of which delivers two complementary clues to retrieve the supporting facts from external knowledge base (KB), which are further encoded into a continuous embedding space using a content-addressable memory. Afterwards, mutual interactions between visual-semantic representation and the supporting facts stored in memory are captured to distill the most relevant information in three modalities (i.e., image, question, and KB). Finally, the optimal answer is predicted by choosing the supporting fact with the highest score. We conduct extensive experiments on two widely-used benchmarks. The experimental results well justify the effectiveness of MCR-MemNN, as well as its superiority over other KB-VQA methods

The multi-tiered medical education system and its influence on the health care market—China’s Flexner Report

BACKGROUND: Medical education is critical and the first step to foster the competence of a physician. Unlike developed countries, China has been adopting a system of multi-tiered medical education to training physicians, which is featured by the provision of an alternative lower level of medical practitioners, or known as a feldsher system since the 1950s. This study aimed to illustrate the impact of multi-tiered medical education on both the equity in the delivery of health care services and the efficiency of the health care market. METHODS: Based on both theoretical reasoning and empirical analysis, this paper documented evidence upon those impacts of the medical education system. RESULTS: First, the geographic distribution of physicians in China is not uniform across physicians with different educational training. Second, we also find the evidence that high-educated doctors are more likely to be hired by larger hospitals, which in turn add the fuel to foster the hospital-center health care system in China as patients choose large hospitals to chase good doctors. Third, through the channels of adverse selection and moral hazard, the heterogeneity in medical education also imposes costs to the health care market in China. DISCUSSION: Overall, the three-tiered medical education system in China is a standard policy trade-off between quantity and quality in training health care professionals. On the one hand, China gains the benefit of increasing the supply of health care professionals at lower costs. On the other hand, China pays the price for keeping a multi-tiered medical education in terms of increasing inequality and efficiency loss in the health care sector. Finally, we discuss the potential policy options for China to mitigate the negative impact of keeping a multi-tiered medical education on the performance of health care market

The multi-tiered medical education system and its influence on the health care market - a China’s flexner report

Background: Medical education is critical and the first step to foster the competence of a physician. Unlike developed countries, China has been adopting a system of multi tiered medical education to training physicians which is featured by the provision of an alternative lower level of medical practitioners , or known as a feldsher system since 1950s This study aimed to illustrate the impact of multi tiered medical education on both the equity in the delivery of health care services and the efficiency of the health care market. Methods Based on both theoretical reasoning and empirical analysis, this paper documented evidences upon those impacts of the medical education system. Results First, the geographic distribution of physicians in China is not uniform across physicians with different education training. Second, we also find the evidence that high educated doctors are more likely to be hired by larger hospitals, which in turn add the fuel to foster the hospital center health care system in China as pati ents choose large hospitals to chase good doctors. Third, through the channels of adverse selection and moral hazard, the heterogeneity in medical education also imposes costs to the health care market in China. Discussion Overall, the three tiered medical education system in China is a standard policy trade off between quantity and quality in training health care professionals. On one hand, China gains the benefit of increasing supply of health care professionals at lower costs. On the other hand, C hina pays the price for keeping a multi tiered medical education in terms of increasing inequality and efficiency loss in the health care sector. Finally, we discuss the potential policy options for China to mitigate the negative impact of keeping a multi tiered medical education on the performance of health care market

Cross-Modal Reasoning with Event Correlation for Video Question Answering

Video Question Answering (VideoQA) is a very attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i.e., the spatio-temporal video content and the word sequence in question. Although various attention mechanisms have been utilized to manage contextualized representations by modeling intra- and inter-modal relationships of the two modalities, one limitation of the predominant VideoQA methods is the lack of reasoning with event correlation, that is, sensing and analyzing relationships among abundant and informative events contained in the video. In this paper, we introduce the dense caption modality as a new auxiliary and distill event-correlated information from it to infer the correct answer. To this end, we propose a novel end-to-end trainable model, Event-Correlated Graph Neural Networks (EC-GNNs), to perform cross-modal reasoning over information from the three modalities (i.e., caption, video, and question). Besides the exploitation of a brand new modality, we employ cross-modal reasoning modules for explicitly modeling inter-modal relationships and aggregating relevant information across different modalities, and we propose a question-guided self-adaptive multi-modal fusion module to collect the question-oriented and event-correlated evidence through multi-step reasoning. We evaluate our model on two widely-used benchmark datasets and conduct an ablation study to justify the effectiveness of each proposed component

Domain Adaptation with Incomplete Target Domains

Domain adaptation, as a task of reducing the annotation cost in a target domain by exploiting the existing labeled data in an auxiliary source domain, has received a lot of attention in the research community. However, the standard domain adaptation has assumed perfectly observed data in both domains, while in real world applications the existence of missing data can be prevalent. In this paper, we tackle a more challenging domain adaptation scenario where one has an incomplete target domain with partially observed data. We propose an Incomplete Data Imputation based Adversarial Network (IDIAN) model to address this new domain adaptation challenge. In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption. We conduct experiments on both cross-domain benchmark tasks and a real world adaptation task with imperfect target domains. The experimental results demonstrate the effectiveness of the proposed method

Consumers’ attitudes toward benefits and drawbacks of vehicle-to-grid technology:An agent-based model

Vehicle-to-Grid (V2G) is an important technology for Electric Vehicles (EVs) and the power grid. This paper first provided insights into people’s attitudes toward three key benefits and two key drawbacks of V2G, using survey data collected in Beijing in 2020. Further, we incorporated the empirical findings into a spatial agent-based joint model of EV and V2G adoption to explore how changes in people’s attitudes toward the benefits and drawbacks could influence the adoption of V2G. The survey results suggested that people tended to be most concerned about battery degradation and least concerned about grid support. Our diffusion simulation suggests that mitigating BEV owners’ concerns about battery degradation and enhancing public awareness of the cost-saving potential of V2G can significantly increase the number of people will/may adopt V2G with a BEV and a PHEV, respectively. However, these attitudinal improvements do not lead to a notable rise in V2G adopters. Furthermore, V2G tended to diffuse more easily across Plug-in Hybrid EV (PHEV) owners than Battery EV (BEV) owners. The results are expected to be helpful for shaping policies to promote the adoption of V2G.<br/

Human Pose Transfer with Augmented Disentangled Feature Consistency

Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguity and appearance inconsistency. To alleviate the current limitations and improve the quality of the synthesized images, we propose a pose transfer network with augmented Disentangled Feature Consistency (DFC-Net) to facilitate human pose transfer. Given a pair of images containing the source and target person, DFC-Net extracts pose and static information from the source and target respectively, then synthesizes an image of the target person with the desired pose from the source. Moreover, DFC-Net leverages disentangled feature consistency losses in the adversarial training to strengthen the transfer coherence and integrates a keypoint amplifier to enhance the pose feature extraction. With the help of the disentangled feature consistency losses, we further propose a novel data augmentation scheme that introduces unpaired support data with the augmented consistency constraints to improve the generality and robustness of DFC-Net. Extensive experimental results on Mixamo-Pose and EDN-10k have demonstrated DFC-Net achieves state-of-the-art performance on pose transfer.Comment: 22 pages, 6 figure