Objective To investigate the current status of clinical nursing risks to nursing students, so as to provide scientific references for medical universities and policy makers. Methods The self-designed questionnaires were distributed to 360 clinical nursing students in four Third-Level hospitals. The group leaders were trained as investigators regularly. All the other nursing students were asked to fill in the questionnaire alone within 30 minutes. The questions issued were the following four aspects: general information of nursing students, clinical intern operating situation, nursing defects and awareness of nursing risks. Results Among the total 400 questionnaires distributed, 360 valid ones were retrieved (90%). The analysis showed all 21 nursing operations could be performed by students alone, and 80% of the nursing students believed that they were able to do 11 routine operations alone, such as bed-making and vital sign monitoring. The incidence rate of eight nursing defects was between 0.56% and 12.22%. As for the four questions about nursing risks, 49.44% of the nursing students operated alone before getting themselves ready because of their eagerness for such experiences. According to the nursing students, the departments with the highest nursing risks ranked as emergency, operating theatre, surgery, pediatrics, internal medicine, and gynecology and obstetrics. Conclusion The current situation of clinical nursing risks to nursing interns is still far from being satisfactory, so it is necessary to improve the training at university, regulate the clinical education, and ensure the intern safety for nursing students.
Objective To develop a Matlab toolbox to improve the efficiency of musculoskeletal kinematics analysis while ensuring the consistency of musculoskeletal kinematics analysis process and results. Methods Adopted the design concept of “Batch processing tedious operation”, based on the Matlab connection OpenSim interface function ensures the consistency of musculoskeletal kinematics analysis process and results, the functional programming was applied to package the five steps for scale, inverse kinematics analysis, residual reduction algorithm, static optimization analysis, and joint reaction analysis of musculoskeletal kinematics analysis as functional functions, and command programming was applied to analyze musculoskeletal movements in large numbers of patients. A toolbox called LLMKA (Lower Limbs Musculoskeletal Kinematics Analysis) was developed. Taking 120 patients with medial knee osteoarthritis as the research object, a clinical researcher was selected using the LLMKA toolbox and OpenSim to test whether the analysis process and results were consistent between the two methods. The researcher used the LLMKA toolbox again to conduct musculoskeletal kinematics analysis in 120 patients to verify whether the use of this toolbox could improve the efficiency of musculoskeletal kinematics analysis compared with using OpenSim. Results Using the LLMKA toolbox could analyze musculoskeletal kinematics analysis in a large number of patients, and the analysis process and results were consistent with the use of OpenSim. Compared to using OpenSim, musculoskeletal kinematics analysis was completed in 120 patients using the LLMKA toolbox with only 2 operations were needed to enter the patient body mass data, operating steps decreased by 99.19%, total analysis time by 66.84%, and manual participation time by 99.72%, just need 0.079 1 hour (4 minutes and 45 seconds). Conclusion The LLMKA toolbox can complete a large number of musculoskeletal kinematics analysis in patients with one click in a way that is consistent in process and results with using OpenSim, reducing the total time of musculoskeletal kinematics analysis, and liberating clinical researchers from cumbersome steps, making more energy into the clinical significance of musculoskeletal kinematics analysis results.