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Abstract Background In professional ethics-related events, there are various unpleasant and complex ethical issues that require strong moral courage. Our aim is to identify and describe the potential categories of moral courage among nurses and to clarify the coping styles of nurses under different categories. Method A cross-sectional study was conducted using three data collection tools: a self-designed general information questionnaire, a Chinese version of the Moral Courage Scale, and a Trait Coping Style Questionnaire. Three hundred fourteen nurses from a tertiary hospital in Heilongjiang Province, China, were analysed using potential profile analysis, descriptive analysis, and Mann-Whitney U test data. Result The latent profile analysis (LPA) results indicate that the two-profile model is the most suitable and supports the existence of two different moral courage profiles: the low moral courage group (60.51%) and the high moral courage group (39.49%), with a high relative entropy value (0.922). The results point to a good profile solution, and there are significant differences between the two profiles. The Mann-Whitney U-test results showed that the positive coping scores of the high moral courage group were significantly higher than those of the low moral courage group, and the negative coping scores of the high moral courage group were significantly lower than those of the low moral courage group. Conclusion Our results reveal the heterogeneity of moral courage in the nurse sample and indicate that nurses in the high moral courage group tend to choose positive coping styles, while nurses in the low moral courage group are more likely to develop negative coping emotions. This provides important significance and reference value for nursing managers, who can propose customised management plans based on the types of moral courage of the nursing community and the coping styles under different categories.

Abstract Background Emphasizes the state of academic stress, social support, and self-regulatory fatigue on the physical and mental development of Chinese nursing students, the purpose of this study was to investigate the relationship between these variables and the mediating role of social support in academic stress and self-regulatory fatigue among a group of undergraduate nursing students in Heilongjiang Province, China, in order to provide a theoretical basis for working to reduce nursing students’ self-regulatory fatigue. Methods In this cross-sectional study, 1703 nursing students from various academic years completed the scales of social support, academic stress, and self-regulatory fatigue. In the end, there were 797 valid questionnaires, for a recovery rate of 46.80%. For statistical analysis, the independent t-test, Kruskal Wallis test, and Pearson correlation coefficient were used. In addition, we undertake analyses using structural equation modeling. Results The bulk of nursing students, or 81.4%, are between the ages of 19 and 21. Eighty percent were females. The bulk (93.0%) was comprised of freshmen. Academic stress, social support, and self-regulatory fatigue had total scores of 111.28 ± 29.38, 37.87 ± 6.70, and 45.53 ± 5.55,respectively. Academic stress was correlated with social support and self-regulatory fatigue (all p

Neural networks excel at capturing local spatial patterns through convolutional modules, but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals. In this work, we propose a novel network named filtering module fully convolutional network (FM-FCN), which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise. First, instead of using a fully connected layer, we use an FCN to preserve the time-dimensional correlation information of physiological signals, enabling multiple cycles of signals in the network and providing a basis for signal processing. Second, we introduce the FM as a network module that adapts to eliminate unwanted interference, leveraging the structure of the filter. This approach builds a bridge between deep learning and signal processing methodologies. Finally, we evaluate the performance of FM-FCN using remote photoplethysmography. Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse (BVP) signal and heart rate (HR) accuracy. It substantially improves the quality of BVP waveform reconstruction, with a decrease of 20.23% in mean absolute error ( MAE ) and an increase of 79.95% in signal-to-noise ratio ( SNR ). Regarding HR estimation accuracy, FM-FCN achieves a decrease of 35.85% in MAE , 29.65% in error standard deviation, and 32.88% decrease in 95% limits of agreement width, meeting clinical standards for HR accuracy requirements. The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction. The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Fangfang Zhu et al.)

The incidence and factors related to mobile phone addiction among Chinese medical students were analyzed through meta-analysis. Chinese literature databases (such as China Knowledge Network and VIP Information Resource System) and English literature databases (such as PubMed and Web of Science) were searched for cross-sectional studies on the incidence and factors related to mobile phone addiction, and the required data were extracted. Meta-analysis was performed using a random effects model with RevMan 5.3 statistical software, and publication bias was tested with Stata 12.0. A total of 20 studies were included, including 36,365 study subjects. Among them, there were 10,597 cases of mobile phone addiction with an incidence of 29.14%. The results of the meta-analysis showed that the combined OR values (95% CI) of the factors were: gender 1.070 (1.030-1.120), residence 1.118 (1.090-1.146), school type 1.280 (1.241-1.321), mobile phone use time 1.098 (1.068-1.129), sleep quality 1.280 (1.288-1.334), self-perception of learning 0.737 (0.710-0.767), and family relationship 0.821 (0.791-0.852). The study showed that being a male student from cities and towns, being at a vocational college, excessive use of mobile phones, and poor sleep quality were the risk factors for mobile phone addiction among medical students in China. Positive self-perception of learning and family relationships were protective factors, and more related factors are still controversial and need to be further explored and confirmed., Competing Interests: None., (AJTR Copyright © 2023.)

Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: -0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.