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.

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.

This study aimed to develop a risk-prediction model for second primary skin cancer (SPSC) survivors. We identified the clinical characteristics of SPSC and created awareness for physicians screening high-risk patients among skin cancer survivors. Using data from the 1248 skin cancer survivors extracted from five cancer registries, we benchmarked a random forest algorithm against MLP, C4.5, AdaBoost, and bagging algorithms for several metrics. Additionally, in this study, we leveraged the synthetic minority over-sampling technique (SMOTE) for the issue of the imbalanced dataset, cost-sensitive learning for risk assessment, and SHAP for the analysis of feature importance. The proposed random forest outperformed the other models, with an accuracy of 90.2%, a recall rate of 95.2%, a precision rate of 86.6%, and an F1 value of 90.7% in the SPSC category based on 10-fold cross-validation on a balanced dataset. Our results suggest that the four features, i.e., age, stage, gender, and involvement of regional lymph nodes, which significantly affect the output of the prediction model, need to be considered in the analysis of the next causal effect. In addition to causal analysis of specific primary sites, these clinical features allow further investigation of secondary cancers among skin cancer survivors.

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. Cite this article as: Chen, H. M., Lu, Y. C, & Pai, H. C. (2023). Impact of a care task design program on novice nursing students’ self-reflection and insight, teamwork skills, and holistic nursing competency. Florence Nightingale Journal of Nursing, 31(2), 69-74.

This study explores the integration of design thinking into online problem-based learning (PBL) within the context of nursing education, leveraging Google Jamboard as a facilitative interactive tool. By prioritizing creativity, empathy, and iteration, this innovative approach seeks to bolster student motivation and satisfaction. Employing a quasi-experimental design, the research involved third-year nursing students and compared the efficacy of traditional PBL methods against a hybrid model that integrates design thinking principles. Notably, the experimental group’s interaction with Google Jamboard significantly enhanced their engagement, resulting in notable improvements in both motivation and satisfaction. The findings underscore the benefits of merging online PBL with design thinking, particularly when supported by digital tools, in creating a more vibrant and effective learning environment. This methodology not only stimulates active learning but also fosters vital competencies such as critical thinking and collaboration, which are crucial in the nursing profession. The results of this study advocate for the implementation of cutting-edge teaching methodologies in nursing education and pave the way for further investigation into the enduring impacts of this educational amalgamation.