Department of Nursing, Chi Mei Medical Center, Tainan, Taiwan,Department of Senior Welfare and Services, Southern Taiwan University of Science and Technology, Taiwan,Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan,Department of Nursing, An Nan Hospital, China Medical University, Tainan, Taiwan,Medical School, St. George's University of London, London, United Kingdom,Department of Nursing, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Medicine (Baltimore). 2022 Mar 18;101(11). doi: 10.1097/MD.0000000000028915.
: Numerous studies have identified factors related to nurses’ intention to leave. However, none has successfully predicted the nurse’s intention to quit the job. Whether the intention to quit the job can be predicted is an interesting topic in healthcare settings. A model to predict the nurse’s intention to quit the job for novice nurses should be investigated. The aim of this study is to build a model to develop an app for the automatic prediction and classification of nurses’ intention to quit their jobs.
: We recruited 1104 novice nurses working in 6 medical centers in Taiwan to complete 100-item questionnaires related to the nurse’s intention to quit the job in October 2018. The k-mean was used to divide nurses into 2 classes based on 5 items regarding leave intention. Feature variables were selected from the 100-item survey. Two models, including an artificial neural network (ANN) and a convolutional neural network, were compared across 4 scenarios made up of 2 training sets (n = 1104 and n = 804 ≅ 70%) and their corresponding testing (n = 300 ≅ 30%) sets to verify the model accuracy. An app for predicting the nurse’s intention to quit the job was then developed as a website assessment.
: We observed that 24 feature variables extracted from this study in the ANN model yielded a higher area under the ROC curve of 0.82 (95% CI 0.80-0.84) based on the 1104 cases, the ANN performed better than the convolutional neural network on the accuracy, and a ready and available app for predicting the nurse’s intention to quit the job was successfully developed in this study.
: A 24-item ANN model with 53 parameters estimated by the ANN was developed to improve the accuracy of nurses’ intention to quit their jobs. The app would help team leaders take care of nurses who intend to quit the job before their actions are taken.
We performed ANN on Microsoft Excel, which is rare in the literature. An app was built to display results using a visual dashboard on Google Maps. The animation-featured dashboard was incorporated with the ANN model, allowing an easy understanding of the classification results with visual representations. The category probability curves were uniquely derived from the Rasch rating scale model and launched to the ANN prediction model to display the binary classification, using probability to interpret the prediction results.
许多研究已经确定了与护士离职意愿相关的因素。然而，目前还没有成功预测护士离职意愿的方法。是否可以预测离职意愿是医疗保健领域的一个有趣话题。应该研究一种预测新护士离职意愿的模型。本研究的目的是建立一个模型，用于开发一个应用程序，自动预测和分类护士离职意愿。
我们招募了 1104 名在台湾 6 家医疗中心工作的新护士，于 2018 年 10 月完成了 100 项与离职意愿相关的问卷。采用 k-均值法将护士分为 2 类，基于 5 项关于离职意愿的项目。从 100 项调查中选择特征变量。比较了包括人工神经网络 (ANN) 和卷积神经网络在内的两种模型，它们在由 2 个训练集（n = 1104 和 n = 804 ≅ 70%）和相应的测试集（n = 300 ≅ 30%）组成的 4 个场景中验证了模型的准确性。然后，开发了一个预测护士离职意愿的应用程序作为网站评估。
我们观察到，在基于 1104 例的 ANN 模型中，从这项研究中提取的 24 个特征变量产生了更高的 ROC 曲线下面积 0.82（95%CI 0.80-0.84），ANN 在准确性方面优于卷积神经网络，并且成功开发了一个可用于预测护士离职意愿的现成应用程序。
开发了一种具有 53 个参数的 24 项 ANN 模型，通过 ANN 估计，可以提高护士离职意愿的准确性。该应用程序将有助于团队领导在护士采取行动之前照顾好有离职意愿的护士。
我们在文献中很少见地在 Microsoft Excel 上执行 ANN。构建了一个应用程序，使用 Google Maps 上的可视化仪表板显示结果。动画特色仪表板与 ANN 模型相结合，允许通过视觉表示轻松理解分类结果。类别概率曲线是从 Rasch 评分量表模型中独特得出的，并应用于 ANN 预测模型，以显示二元分类，使用概率来解释预测结果。
