Nursing Interns’ Attitudes Toward, Preferences for, and Use of Diabetes Virtual Simulation Teaching Applications in China: National Web-Based Survey
Bibliographic record
Abstract
BACKGROUND: Diabetes has placed heavy social and economic burdens on society and families worldwide. Insufficient knowledge and training of frontline medical staff, such as nurses, interns, and residents, may lead to an increase in acute and chronic complications among patients with diabetes. However, interns have insufficient knowledge about diabetes management. The factors that affect interns' current level of diabetes-related knowledge are still unclear. Therefore, understanding the behavioral intentions of interns is essential to supporting the development and promotion of the use of virtual simulation teaching applications. OBJECTIVE: This study aimed to identify the determinants of nursing interns' intentions to use simulation-based education applications. METHODS: From December 1, 2020, to February 28, 2021, the web-based survey tool Sojump (Changsha Xingxin Information Technology Co) was used to survey nursing interns in hospitals across China. Two survey links were sent to 37 partner schools in 23 major cities in China, and they were disseminated through participants' WeChat networks. Multiple regression analysis was used to determine the association between demographic information and basic disease information and the use of the application for treating adult patients. RESULTS: Overall, 883 nursing interns from 23 provinces in China responded to the survey. Among them, the virtual simulation utilization rate was 35.6% (314/883) and the awareness rate was 10.2% (90/883). In addition, among the interns, only 10.2% (90/883) correctly understood the concept of virtual simulation, and most of them (793/883, 89.8%) believed that scenario-simulation training or the use of models for teaching are all the same. Multiple regression analysis showed that the educational level, independent learning ability, and professional identity of the interns were related to use of the application (P<.05). Skills and knowledge that the interns most wanted to acquire included the treatment of hypoglycemia (626/883, 70.9%), functional test simulation (610/883, 69.1%), and blood glucose monitoring technology (485/883, 54.9%). A total of 60.5% (534/883) of the interns wanted to acquire clinical thinking skills, while 16.0% (141/883) wanted to acquire operational skills. Nursing trainees believed that the greatest obstacles to virtual simulation included limited time (280/883, 31.7%), the degree of simulation (129/883, 14.6%), the demand for satisfaction (108/883, 12.2%), and test scores (66/883, 7.5%). CONCLUSIONS: The understanding and usage rate of diabetes virtual simulation teaching applications by Chinese nursing interns is very low. However, they have high requirements regarding this teaching method. Conducting high-quality randomized controlled trials and designing applications that are suitable for the needs of different nurse trainees will increase students' interest in learning and help improve diabetes knowledge among nursing interns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".