ObjectiveTo understand the levels of and influencing factors for knowledge of earthquake in the elderly population. MethodPeople who were older than 60 years in the earthquake area of Sichuan Province were surveyed by self-designed earthquake knowledge scale composed of 6 items with a point of 1-5 for each item between October and November 2013. The total score ranged from 6 to 30 points. ResultsUp to 1 509 elderly people participated the survey. The total score of earthquake knowledge was 13.33±4.85. The main resource for acquiring those knowledge was TV (76.87%). Occupation, education level, residency, marital status, residence, self-injury, health status, access to earthquake knowledge, and worrying about earthquake were related factors for their knowledge on earthquake (P<0.05). Regression analysis showed that the higher degree of education, having a spouse, living in city, more access to earthquake knowledge, worrying about earthquake were the promotion factors for earthquake knowledge preparedness. Self-injury, poor health were the factors that hindered the acquiring of earthquake knowledge preparedness. ConclusionsIt is necessary to train knowledge of earthquake for elderly people individually because they lack enough resources and tend to be affected by many factors.
ObjectiveTo understand the pre-hospital emergency medical staff's knowledge on crush injury and crush syndrome, and the influence of active and effective pre-hospital measures on the prognosis of patients with crush injury. MethodsWe retrospectively analyzed the clinical data of 51 patients with crush injury treated from September 2004 to August 2014, and recorded the number of cases in which pre-hospital emergency medical staff recognized and/or took effective measures to control crush syndrome. Treatment group included those patients who accepted effective prevention and control measures, and the rest of the patients were included in the control group. We compared the two groups of patients in terms of the incidence of serious complications such as crush syndrome and amputation. ResultsTwenty-five cases (49.0%) of crush injury were recognized before the patients were admitted into the hospital, among whom 20 (39.2%) accepted effective preventive and control measures. The mangled extremity severity score between the two groups of patients had no significant difference (6.69±1.96 vs. 7.23±3.54, P>0.05). After being admitted into the hospital, the treatment group had one complication case of crush injury, while the control group had 10 complication cases including 7 of crush injury and 3 of amputation. The complication rate of the treatment group (5.0%) was significantly lower than that of the control group (32.3%, P<0.05). ConclusionActive and effective prehospital preventive and control measures are very important in the treatment of crush syndrome and reduction of morbidity, but the pre-hospital emergency personnel's knowledge of crush injury and crush syndrome is not enough.
Knowledge translation (KT) provides a paradigm to bridge the gap between knowledge and practice, which has critical instructive significance for health promotion. This article expounds on the connotation of KT by comparing it with similar terms. Next, it introduces three kinds of common KT theoretical models, including process models, determinant frameworks, and evaluation frameworks. Finally, its application and experiences in health promotion are summarized to provide references for the ongoing health promotion in China.
ObjectiveTo investigate the status of knowledge, attitude, and practice of patient identification in nurses, and provide a basis for clinical managers to carry out targeted training.MethodsA total of 3 696 nurses of tertiary, secondary, and primary hospitals in Guizhou Province were recruited and investigated for the status of knowledge, attitude, and practice of patient identification with a questionnaire by using convenient sampling in May 2019.ResultsThe scores of identification knowledge, attitude, and practice of the 3 696 nurses were 47.87±6.10, 27.39±3.15, and 57.19±4.86, respectively. Logistic regression analysis showed that the higher the educational level was, the higher the score of nurses’ knowledge of patient identification was [odds ratio (OR)=1.592, 95% confidence interval (CI) (1.084, 2.338), P=0.018]; the higher the personal monthly income was, the more positive the nurses’ attitude towards patient identification was [OR=1.570, 95%CI (1.005, 2.453), P=0.048].ConclusionsThe general situation of patient identification in nurses is good, but there are still differences among nurses with different characteristics. It is suggested that managers should pay special attention to the training of nurses with low educational level and low income, make them master the knowledge of patient identification, at the same time, improve their enthusiasm and standardize their behavior, so as to ensure the safety of patients.
Rare diseases have problems with low number of cases, low social awareness, and long time of diagnosis. “Targeted doctor” is the first step to help rare disease patients start the correct path of diagnosis and treatment. This article introduces the design of a decision-making engine for patients with rare diseases by constructing a knowledge graph of rare diseases and experts, using an intelligent question-and-answer system, and combining big data and artificial intelligence methods. This engine can perform rare disease pre-screening based on patient portraits and other information, and recommend the best visiting route to patients, thereby improving the efficiency of rare disease patients’ medical service system and enhancing the decision-making ability of rare diseases.
ObjectiveTo investigate the knowledge and attitude of medical professionals in various regions of China on obstructive sleep apnea (OSA) and to find out the influence of sleep center setting on the above results.MethodsA self-designed questionnaire based on OSAKA questionnaire was designed. A total of 630 medical staff were investigated in 7 hospitals at different levels in various regions in China. The subjects were divided into two groups according to whether they had sleep center (including sleep monitoring room) or not. Survey data were analyzed.ResultsA total of 630 questionnaires were sent out, and 590 valid questionnaires were received, and the effective response rate was 93.65%. About half of those surveyed had sleep centers in the hospitals where they worked. There was no significant difference in three attitude problems and the choice of continuous positive airway pressure and surgical treatment between the two groups (all P>0.05). Subjects whose hospital had no sleep center were more prone to select weight loss (estimated parameters=0.513, P=0.046), no smoking and wine (estimated parameter=0.472, P=0.040), avoidance of overwork (estimated parameter=0.933, P=0.000), and drug (estimated parameter=0.802, P=0.000). The average correct rate of OSA knowledge was 45.59%±20.68%. Among them, the correct rate of response to treatment measures was the highest, and the correct rate of other knowledge points was poor. The average correct rate of total accuracy, symptoms and target organ damage in subjects whose hospital had sleep center was higher than that in subjects whose hospital had no sleep center, and there were significant differences (P=0.001, P=0.012, P=0.000). There was a positive correlation between the knowledge of OSA and their attitude towards OSA, treatment and further understanding of the knowledge (r=0.247, P=0.000).ConclusionIt is necessary to strengthen propaganda and education of OSA, and the establishment of sleep center is helpful for medical personnel to know more about OSA and to develop sleep medicine.
In combination with the national health informatization construction in UK during the past ten years, this article introduced the resource construction of decision making knowledge library like British Electronic Medicine Library Clinical Pathway Database and NHS Evidence, as well as the function and application of clinical decision support system (CDSS) like PRODIGY, medical knowledge map and so on, discussed the development characteristics and construction experiences of British health decision support system (HDSS). And aiming directly at Chinese specific circumstances, this article offered some suggestions on promoting China HDSS development, for instance, dynamically integrating CDSS with patients’ diagnosis and treatment procedure through the electronic medical record system, strengthening the resources construction of knowledge library, establishing localized clinical pathway, and so on.
ObjectiveTo explore the knowledge and attitude of pain management in undergraduate nursing students, analyze the influencing factors, and improve the future education of the undergraduate nursing students. MethodsA total of 220 undergraduate nursing students were investigated with the Knowledge and Attitudes Survey Regarding Pain between November 2014 to June 2013. ResultsUndergraduate nursing students were lack of cognition on pain management and attitude, with an average wrong answer rate of 55.70%; the difference in reading related books or journals in pain, pain management training, and frequency of usage of pain assessment tools among the influential factors were significant (P < 0.05). The most common factor was the lack of pain management training. ConclusionsThe knowledge level of pain management in undergraduate nursing students who are lack of pain management training needs to be improved. Medical schools might optimize pain management courses, and hospitals should enhance the pain management training of clinical nurses so as to make them assess patients correctly by using pain assessment tools. In addition, it's necessary to enhance the nursing students' pain management practice during the clinical practice, so as to improve the pain management knowledge level in undergraduate nursing students.
Glaucoma is one of blind causing diseases. The cup-to-disc ratio is the main basis for glaucoma screening. Therefore, it is of great significance to precisely segment the optic cup and disc. In this article, an optic cup and disc segmentation model based on the linear attention and dual attention is proposed. Firstly, the region of interest is located and cropped according to the characteristics of the optic disc. Secondly, linear attention residual network-34 (ResNet-34) is introduced as a feature extraction network. Finally, channel and spatial dual attention weights are generated by the linear attention output features, which are used to calibrate feature map in the decoder to obtain the optic cup and disc segmentation image. Experimental results show that the intersection over union of the optic disc and cup in Retinal Image Dataset for Optic Nerve Head Segmentation (DRISHTI-GS) dataset are 0.962 3 and 0.856 4, respectively, and the intersection over union of the optic disc and cup in retinal image database for optic nerve evaluation (RIM-ONE-V3) are 0.956 3 and 0.784 4, respectively. The proposed model is better than the comparison algorithm and has certain medical value in the early screening of glaucoma. In addition, this article uses knowledge distillation technology to generate two smaller models, which is beneficial to apply the models to embedded device.
To address the issue of a large number of network parameters and substantial floating-point operations in deep learning networks applied to image segmentation for cardiac magnetic resonance imaging (MRI), this paper proposes a lightweight dilated parallel convolution U-Net (DPU-Net) to decrease the quantity of network parameters and the number of floating-point operations. Additionally, a multi-scale adaptation vector knowledge distillation (MAVKD) training strategy is employed to extract latent knowledge from the teacher network, thereby enhancing the segmentation accuracy of DPU-Net. The proposed network adopts a distinctive way of convolutional channel variation to reduce the number of parameters and combines with residual blocks and dilated convolutions to alleviate the gradient explosion problem and spatial information loss that might be caused by the reduction of parameters. The research findings indicate that this network has achieved considerable improvements in reducing the number of parameters and enhancing the efficiency of floating-point operations. When applying this network to the public dataset of the automatic cardiac diagnosis challenge (ACDC), the dice coefficient reaches 91.26%. The research results validate the effectiveness of the proposed lightweight network and knowledge distillation strategy, providing a reliable lightweighting idea for deep learning in the field of medical image segmentation.