Learning curves for itinerant nurses to master the operation skill of Ti-robot-assisted spinal surgery equipment by CUSUM analysis: A pilot study.

This study aimed to investigate the minimum number of operations required for itinerant nurses in the operating room to master the skills needed to operate the Ti-robot-assisted spinal surgery equipment. Additionally, we aimed to provide a corresponding basis for the development of qualification admission criteria and skill training for nurses who cooperate with this type of surgery. Nine operating room itinerant nurses independently performed Ti-robot equipment simulations using a spine model as a tool, with 16 operations per trainee. Four evaluation indices were recorded: time spent on equipment preparation and line connections, time spent on image acquisition and transmission, time spent on surgical spine screw placement planning, and time spent on robot arm operation. Individual and general learning curves were plotted using cumulative sum analysis. The number of cases in which the slope of the individual learning curves began to decrease was 3-11 cases, and the number of cases in which the slope of the general learning curve began to decrease was 8 cases. The numbers of cases in which the learning curves began to decrease in the four phases were the 5th, 8th, 11th, and 3rd cases. Itinerant nurses required at least eight cases to master the equipment operation skills of Ti-robot-assisted spinal surgery. Among the four phases, the image acquisition and transmission phases and the surgical spine screw placement planning phase were the most difficult and must be emphasized in future training.

Inhibition and activation of interactions in networked weak prisoner's dilemma.

In the framework of the coevolution dynamics of the weak prisoner's dilemma, inspired by prior empirical research, we present a coevolutionary model with local network dynamics in a static network framework. Viewing the edges of the network as social interactions between individuals, when individuals play the weak prisoner's dilemma game, they accumulate both payoffs and social interaction willingness based on a payoff matrix of the social interaction willingness we constructed. The edges are then inhibiting or activating based on the social interaction willingness of the two individuals, and individuals only interact with others through activated edges, resulting in local network dynamics in a static network framework. Individuals who receive more cooperation will be more likely to activate the edges around them, meaning they will participate in more social interactions. Conversely, individuals who receive more defects will do the opposite. Specifically, we investigate the evolutionary dynamics of cooperation under different levels of sensitivity to social interaction willingness and the temptation to defect. Through the simulation, we find that sparse cooperator clusters can expand greatly when social interaction sensitivity and temptation to defect are low. In contrast, dense cooperator clusters form rapidly in a high social interaction sensitivity, which protects the cooperation from high temptation.