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To evaluate the effect of video-assisted self-assessment on nursing students' knowledge and skills in nasogastric catheter (NGS) insertion. Psychomotor skills are critical in nursing education, but traditional teaching methods often limit students' practice opportunities. Video-assisted learning, especially self-assessment, is an innovative approach for enhancing clinical competencies, yet its use specifically for nursing skill development is under-researched. Parallel-group Randomized Controlled Trial (Clinical Trials ID: NCT06494267). This study involved 65 first-year nursing students at Atatürk University, assigned to an intervention group (video feedback for self-assessment, n = 31) and a control group (no video feedback, n = 34). Both groups received theoretical and practical NGS training. In the intervention group, students' NGS insertion performances were recorded and reviewed for self-evaluation, whereas the control group received no video feedback. Data were collected using a knowledge test, OSCE-based skill checklist and satisfaction questionnaire. Statistical analyses included t-tests and effect size calculations. Both groups showed significant pre-to-post-test improvements in knowledge and skills (p < 0.05). The intervention group achieved a greater increase in knowledge (mean 60.64 ± 15.53) compared with the control (57.20 ± 18.14; p = 0.417, d=0.203) and a significantly larger improvement in OSCE scores (intervention: 87.61 ± 7.25; control: 61.29 ± 17.51; p < 0.001, d=1.931). Additionally, 94.19 % of intervention group students reported that video-assisted self-assessment facilitated deficiency identification, with 93.55 % noting its support for objective evaluation. Video-assisted self-assessment significantly enhances NGS placement knowledge, skill performance and self-confidence, demonstrating its effectiveness for improving nursing education outcomes. [ABSTRACT FROM AUTHOR]

Through a comparative study of Turkey and Israel, this article highlights a specific strategy that autocratizing populist incumbents in ethnically divided societies utilize when they face election setbacks. A "blood gambit" entails fomenting violent conflict to keep the opposition divided along identitarian cleavages, while creating a rally-around-the-flag effect to help the incumbent win a renewed election. After failing to secure a parliamentary majority in June 2015, Erdoğan ended the Kurdish peace process and engineered repeat elections amidst heightened nationalist fervour and renewed conflict with the Kurdistan Workers' Party (PKK). These elections gave the Justice and Development Party (AKP) a majority and marked the beginning of its alliance with the ultranationalists. Following Israel's March 2021 elections, Netanyahu increased state repression of Palestinians, which triggered interethnic violence and renewed confrontation with Hamas. The violence threw a wrench into coalition-building efforts between ideologically and ethnically diverse opposition parties. The comparison of Israel and Turkey as two countries with different majority religions, ethnic compositions and socioeconomic levels shows that "blood gambit" is not a parochial strategy. Our analysis also demonstrates that the outcomes of these strategies are shaped by differing institutional and political contexts, in particular, the extent of executive aggrandizement and the level of party fragmentation. [ABSTRACT FROM AUTHOR]

Aim: The purpose of this study is to compare the efficacy of an artificial intelligence (AI)-based care plan learning strategy with standard training techniques in order to determine how it affects nursing students' learning results in newborn resuscitation., Methods: Seventy third-year nursing students from a state university in Türkiye participated in the study. They were split into two groups: the experimental group, which received care plans based on AI, and the control group, which received traditional instruction. The control group underwent traditional training consisting of lectures and skill demonstrations, while the experimental group underwent 4 weeks of training utilising an AI-based care plan learning approach. Neonatal resuscitation knowledge tests and student information questionnaires were used for pre- and post-test assessments., Results: When compared to the control group, the AI-based care plan group demonstrated noticeably greater learning achievement in newborn resuscitation. While the two groups' pre-test results were comparable, the AI-based education group's post-test results were noticeably higher than those of the traditional education group. Furthermore, most of the students had favourable opinions on AI applications and acknowledged their advantages for the nursing field., Conclusion: The study's conclusions highlight the benefits of incorporating AI technology into nursing education and highlight how it might improve student learning outcomes for vital competencies like newborn resuscitation., (© 2024 John Wiley & Sons Ltd.)