Objective: Caring for the disabled elderly is a source of stress for family caregivers, and the lack of social support and the pressure of social exclusion might aggravate family caregiver burden. The purpose of this study was to examine the association between caring load and family caregivers’ burden, as well as the mediating effects of social support and social exclusion.Methods: Data used in this study derived from the nationally representative database of aged population in China, and 3125 households with family caregivers for the home-bound disabled elderly were eventually selected into this analysis. Regression methods and mediation analysis methods were employed in this study.Results: The results indicated that there was a significant positive association between caring load and the caregiver burden, and specifically, social support intensity (rather than social support breadth) and passive social exclusion (rather than active social exclusion) played partial mediating effects. Furthermore, the contributions of mediating effects of social support intensity and passive social exclusion were 13-15% and 27%-29%, respectively, and the total contribution of mediating effects was about 35%-38%.Conclusion: Family caregivers’ burden should be paid more attention in the large population with rapidly aging speed like China, and more guidance services as well as support should be provided to family caregivers. In addition, it is crucial to focus on the social support and social exclusion in community in the public policy innovation.
Background Cervical cancer cell detection is an essential means of cervical cancer screening. However, in thin-prep cytology test-based images, the detection accuracy of traditional computer-aided detection algorithms is typically low due to the overlap of cells with blurred cytoplasmic boundaries. Clinical applications face more difficulties. Method we propose a cervical cancer cell detection network (3cDe-Net) based on an improved backbone network and multiscale feature fusion, and the network consists of the backbone network and the detection head. In the backbone network, a dilated convolution and a group convolution are introduced to improve the resolution and model expression ability. In the detection head, multiscale features are obtained based on a feature pyramid fusion network to ensure accurate capture of small cells; and then, based on Faster R-CNN, adaptive anchors of cervical cancer cells are generated via unsupervised clustering; furthermore, a new balanced L1-based loss function is defined, which reduces the unbalanced sample contribution loss. Result For two different datasets(the Data-T dataset and Herlev dataset), the baselines including ResNet-50, ResNet-101, Inception-v3, ResNet-152 and the feature concatenation network were used, and the final quantitative results showed the effectiveness of the proposed backbone network DC-ResNet. Furthermore, experiments with both datasets show that our detection network 3cDe-Net, based on the optimal anchor, the defined new loss function, and the DC-ResNet, outperforms existing methods and achieves a mAP of 50.4%. By horizontal comparison of cells on the image, the category information and location information can be obtained concurrently. Conclusion The proposed 3cDe-Net is an end-to-end network that can detect cancer cells on multicell pictures and can determine their locations. The model processes and analyzes samples directly at the picture level rather than the cellular level, which is more efficient and meets clinical needs more effectively.
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