Background Ethics education can be developed in undergraduate nursing curriculum using a variety of teaching and learning strategies, and the content of narrative-based stories has rarely been evaluated in ethics courses. Objective This study aimed to compare the effect of teaching ethical principles through narrative ethics and lectures on the moral sensitivity of undergraduate nursing students. Methods This was a pretest and posttest quasi-experimental study with a control group. A total of 105 undergraduate nursing students from the nursing department of (...) Kerman University of medical science were included in the study. The narrative-based story and lecture lasted for four two-hour sessions. The outcomes were measured by a moral sensitivity questionnaire. Results The study results showed that teaching ethical principles had a significant effect on the mean score of moral sensitivity in the intervention groups after the intervention, but it was not significant in the control group. Based on the mean score of moral sensitivity, the students in the narrative story group got a higher score in all subscales of moral sensitivity questionnaire. Conclusion Based on this study’s findings, it can be concluded that narrative-based education is an effective way for undergraduate nursing students to promote their moral sensitivity and the ability to make ethical decisions. (shrink)

The dynamic nature and increasing complexity of the construction industry have led to increased conflicts in construction projects. An accurate prediction of the outcome of a dispute resolution in courts could effectively reduce the number of disputes that would otherwise conclude by spending more money through litigation. This study aims to introduce a two-layered fuzzy logic model for predicting court decisions in construction contract disputes. 100 cases of construction contract disputes are selected from the courts of Iran. A questionnaire survey (...) is then conducted to extract a set of fuzzy rules for identifying important decision parameters and expert knowledge. Accordingly, a two-layered fuzzy logic-based decision-making architecture is proposed for the prediction model. Furthermore, the fuzzy system is trained based on 10-fold cross-validation. Analysis of results indicates that 51 out of the 100 cases are filed after the dissolution and termination of the contract show a significant impact of these clauses as the root cause in construction contract disputes. Our results present a proposed hierarchical fuzzy system that can correctly predict nearly 60% of the test data. Also, we demonstrate a methodology of using argument before ML to establish interpretable AI models. Based on our findings, a fuzzy model with a hierarchical structure may be used as a simple and efficient method for predicting court decisions in construction contract disputes. (shrink)