Experience is crucial for enhancing task performance. However, rescue experience, as a key contributor to individual and team capabilities within fire brigades, has not received sufficient attention in fire safety management research. Based on the concept of shared team task-specific (STTS) experience, this study explored how it affects the rescue effectiveness of fire brigades. To test the proposed model, data were collected from a sample of 452 firefighters in China. The results indicated that STTS experience, including both task-specific experience and shared team experience, significantly improves the rescue effectiveness of fire brigades. Moreover, shared mental models, including both teammate mental models and task mental models, mediate the relationship between STTS experience and rescue effectiveness, while team tenure strengthens the effect of STTS experience on shared mental models and subsequent team performance. These findings enhance the understanding of fire brigade performance and contribute to improving emergency rescue effectiveness. Theoretical and practical implications, limitations, and future research directions are provided and discussed.

Although deep learning based X-ray bone age detection algorithms have achieved good results, the complexity of these SOTA methods is also increasing. In this article, we propose a new lightweight network that exceeds the accuracy of the SOTA algorithm, but significantly reduces computational complexity. Our method has an average error of 0.18 years and a parameter quantity of only 0.9M, which outperforms the existing best methods in terms of computational performance and accuracy.

In the context of Disease X risks, how governments and public health authorities make policy choices in response to potential epidemics has become a topic of increasing concern. The tightness of epidemic prevention policies is related to the effectiveness of the implementation of measures, while the organizational cognition of epidemic risks is related to the rationality of policy choices. During the three years of COVID-19, the Chinese government constantly adjusted the tightness of its prevention policies as awareness of the epidemic risk improved. Therefore, based on the epidemic risk organizational cognition model, the key nodes that affect the tightness of epidemic prevention policies can be explored to find the organizational behavior rules behind the selection of prevention policies. Firstly, through observing the adjustments made to the Chinese government’s prevention strategies during the epidemic, a time-series cross-case comparative analysis reveals how policy tightness shifted from stringent to lenient. This shift coincided with the organizational cognition of epidemic risk evolving from vague to clear. Secondly, by building the “knowledge-cognition” coordinate system to draw the organizational cognition spiral of epidemic risk, it is clear that the changes in the tightness of the prevention policies mainly came from the internalization and externalization of knowledge such as epidemic risk characteristics to promote the level of organizational cognition, which is manifested as expansion and deepening. Thirdly, the node changes in the interaction between organizational cognition development and policy choice proved that different stages of the epidemic had diverse environmental parameters. Moreover, as the epidemic nears its end, the focus of policy tightness is shifting from policy objectives to policy implementation around governance tools. The results indicate that organizational cognition of epidemic risk exhibits significant stages and periodicity. Additionally, epidemic risk characteristics, environmental coupling, and governance tools are crucial factors in determining the tightness of epidemic prevention policies.