Impact of a Care Task Design Program on Novice Nursing Students' Self-Reflection and Insight, Teamwork Skills, and Holistic Nursing Competency.

AIM: The aims of this study were to evaluate the effects of a self-appraisal of clinical simulation care tasks in novice nursing students and assess their self-reflection and insight, teamwork skills, and holistic nursing competence in four different periods. METHOD: A single group pre- and post-test design was conducted. Data were collected between September 2019 and February 2020. Nursing students who participated in the fundamental nursing laboratory courses in the second year of the nursing department at a medical university were invited to participate in the study. Data were collected at four time points using the Self-Reflection and Insight Scale, Holistic Nursing Competence Scale, and the Teamwork Skills Scale. A generalized estimating equation was used for all statistical analyses. RESULTS: Across the four measurements, the score of self-reflection and insight ranged from 76.68 to 78.00, teamwork skills from 68.83 to 71.21, and holistic nursing competence from 134.48 to 146.46. Student performance was above average on all research variables. The results confirm the hypotheses that the program improves self-reflection and insight, teamwork skills, and holistic nursing competencies in nursing students. CONCLUSION: These findings suggest that the program can be used to improve students' self-reflection, and it may also help to enhance their teamwork skills and holistic nursing competence.

Effects of Problem-Based Learning Strategies on Undergraduate Nursing Students' Self-Evaluation of Their Core Competencies: A Longitudinal Cohort Study.

To respond to patients' increasing demands and strengthen nursing professionals' capabilities, nursing students are expected to develop problem-solving skills before they enter the workforce. Problem-based learning (PBL) is expected to provide effective simulation scenarios and realistic clinical conditions to help students achieve those learning goals. This article aims to explore the effects of PBL strategies on nursing students' self-evaluation of core competencies. This longitudinal cohort survey study evaluated 322 nursing students attending Chung Shan Medical University, Taiwan, in 2013 and 2014, where PBL teaching strategies are used in all four undergraduate years from freshman to senior. Based on their undergraduate academic levels, students were categorized into three groups- one-year PBL exposure, two-year PBL exposure, and three-year exposure. A core competency questionnaire was administered twice to ask participants to self-assess five professional competencies: learning attitude, problem identification, information analysis, execution, and life-long learning. The results showed that students with the longest exposure to PBL (Group 3) had higher self-evaluated scores for all core competencies than the other groups, except for the execution competency. The mean total competency score increased by 0.12 points between the pre-and-test. In addition, the mean score increased significantly more in Group 3 than in Groups 1 and 2. These trends were consistent for the information analysis, execution, and life-long learning competencies. In conclusion, the changes in the self-evaluated scores between groups indicate PBL strategies effectively improve nursing students' core competencies. The longest exposure group reported higher self-evaluated core competency scores than the other groups, especially for the information analysis, execution, and life-long learning competencies.

Childbearing Perceptions Among Taiwanese Women Undergoing In Vitro Fertilization Treatment: A Qualitative Study.

INTRODUCTION: Some infertile women are eager to have children. The purpose of this study was to explore the childbearing perceptions of women who undergo in vitro fertilization (IVF) treatment. METHODOLOGY: Qualitative phenomenological data were collected from 20 infertile women who received IVF treatment in Taiwan. We conducted one-on-one in-depth interviews with the women. Audio recordings were transcribed as textual data and analyzed using Giorgi content analysis until saturation was achieved. RESULTS: Three themes were identified: important tasks related to practicing the family life cycle, ensuring a tight circle of marriage, and the hope to change health. DISCUSSION: Perceptions regarding childbearing in women undergoing IVF treatment in the context of the traditional Chinese fertility culture are multifaceted. To improve the overall integrity of health care provided to infertile women, nurses should be encouraged to consider the cultural connotations and needs of infertile women in the clinic.

An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers.

Endometrial cancer is one of the most common gynecological malignancies in developed countries. The prevention of the recurrence of endometrial cancer has always been a clinical challenge. Endometrial cancer is asymptomatic in the early stage, and there remains a lack of time-series correlation patterns of clinical pathway transfer, recurrence, and treatment. In this study, the artificial immune system (AIS) combined with bootstrap sampling was compared with other machine learning techniques, which included both supervised and unsupervised learning categories. The back propagation neural network, support vector machine (SVM) with a radial basis function kernel, fuzzy c-means, and ant k-means were compared with the proposed method to verify the sensitivity and specificity of the datasets, and the important factors of recurrent endometrial cancer were predicted. In the unsupervised learning algorithms, the AIS algorithm had the highest accuracy (83.35%), sensitivity (77.35%), and specificity (92.31%); in supervised learning algorithms, the SVM algorithm had the highest accuracy (97.51%), sensitivity (95.02%), and specificity (99.29%). The results of our study showed that histology and chemotherapy are important factors affecting the prediction of recurrence. Finally, behavior code and radiotherapy for recurrent endometrial cancer are important factors for future adjuvant treatment.

Machine Learning in Prediction of Second Primary Cancer and Recurrence in Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) is the third commonly diagnosed cancer worldwide. Recurrence of CRC (Re) and onset of a second primary malignancy (SPM) are important indicators in treating CRC, but it is often difficult to predict the onset of a SPM. Therefore, we used mechanical learning to identify risk factors that affect Re and SPM. PATIENT AND METHODS: CRC patients with cancer registry database at three medical centers were identified. All patients were classified based on Re or no recurrence (NRe) as well as SPM or no SPM (NSPM). Two classifiers, namely A Library for Support Vector Machines (LIBSVM) and Reduced Error Pruning Tree (REPTree), were applied to analyze the relationship between clinical features and Re and/or SPM category by constructing optimized models. RESULTS: When Re and SPM were evaluated separately, the accuracy of LIBSVM was 0.878 and that of REPTree was 0.622. When Re and SPM were evaluated in combination, the precision of models for SPM+Re, NSPM+Re, SPM+NRe, and NSPM+NRe was 0.878, 0.662, 0.774, and 0.778, respectively. CONCLUSIONS: Machine learning can be used to rank factors affecting tumor Re and SPM. In clinical practice, routine checkups are necessary to ensure early detection of new tumors. The success of prediction and early detection may be enhanced in the future by applying "big data" analysis methods such as machine learning.

[Smart Medicine and Healthcare].

Innovation and rapid technological development in Smart Medicine or Smart Healthcare impact profoundly on many aspects of healthcare. It is believed that Health Information Technology (HIT) has the potential to improve integration between care providers, reduce administrative costs and burdens, reduce medical errors, and improve care quality and patient outcomes. However, issues such as interoperability, compatibility, and integration are critical to effectively integrating hardware and software in order to fully realize the benefits of HIT. High-end medical devices and equipment, including medical carts / mobile computer carts and wireless physiological and biomedical monitoring devices, should also be integrated into the hospital information system. Furthermore, the Data, Information, Knowledge, and Wisdom Hierarchy (DIKW) has been gaining popularity in the development of Nursing Information Systems (NIS) since 2013. To create a DIKW-based information system, data must first be defined and analyzed and then transformed into meaningful information. Eventually, this information is transformed into an intelligent system. For example, if evidence-based nursing research results / findings are integrated into the NIS to guide clinical practice, patient outcomes, patient safety, and healthcare quality will be greatly enhanced.

An Interview and an Observation Study of Nurses and Student Nurses' Electronic Clinical Documentation Behaviors in a OB/GYN Nursing Ward.

This research was conducted to understand clinical nurses' and nursing students' current use of a clinical nursing information system to document their patient care activities in an OB/GYN nursing ward at a medical center in central Taiwan. A semi-structure interview and participant observation methods were used to gather the data. The results showed that the majority of participants regarded the clinical nursing information system as a useful system in terms of saving documentation time and improving communication among healthcare providers. This study provided some empirical evidence of the attitudes/behaviors of nurses and student nurses about electronic documentation. However, more studies were recommended to evaluate the impact of electronic nursing documentation on the quality of care delivered to patients.

Understanding the Learning Needs of New Graduate Nurses at a Surgical Ward to Develop a Smartphone/Table Based Training System.

Taiwan government and health officials had made the new graduate nurses training program a mandatory requirement for all teaching hospitals in order to train new graduate nurse staffs to prepare them for the clinical practice. Mobile devices may provide a feasible and useful tool for the new graduate nurses to learn knowledge/skills, thus, the study researchers proposed to develop a smartphone/tablet based learning system to assist the new graduate nurses in the study hospital ease their transition into the RN roles. Prior to developing this electronic training system, learning needs assessment was carried out. A focus group research method was used to help the researchers better understand the learning needs of new graduate nurses. The data is still under analysis, results and discussed will be reported as soon as the analysis is completed.