Suicide and associated psychosocial correlates among university students in Taiwan: A mixed-methods study.

BACKGROUND/PURPOSE: The trend of suicide rates among young adults has been increasing worldwide. The study aimed to identify the suicide risks and associated psychosocial factors in a large university in Taiwan. METHODS: This is a mixed-methods study using both questionnaire survey and two open-ended questions for the exploration of qualitative data. An online survey was conducted between two periods of the same semester in 2018 to collect different sources of stress and other suicide correlates. The measurement scales included the 9-item Concise Mental Health Checklist, the University Stress Screening Tool in Taiwan and the Chinese Maudsley Personality Inventory. The participants were required to fulfill two open-ended questions about stress experience and depressive symptoms in the previous month in the end of the questionnaire, which was analyzed using thematic analysis. RESULTS: A total of 857 university students were recruited (67.9% female participants). Over a quarter of participants were under poor mental health status and more than 60% experienced stressful events in the prior year. A higher suicide risk and neurotic trait was noticed compared to the general public. These results were consistent with the qualitative findings. CONCLUSION: While identifying several risk factors that cumulatively conduced to higher suicide risks, neuroticism served as a key element in the increased suicide risk among the university students. Suicide prevention strategies for university students should highlight stress management for those with neurotic trait and early suicide risk identification.

The application and use of artificial intelligence in cancer nursing: A systematic review.

PURPOSE: Artificial Intelligence is being applied in oncology to improve patient and service outcomes. Yet, there is a limited understanding of how these advanced computational techniques are employed in cancer nursing to inform clinical practice. This review aimed to identify and synthesise evidence on artificial intelligence in cancer nursing. METHODS: CINAHL, MEDLINE, PsycINFO, and PubMed were searched using key terms between January 2010 and December 2022. Titles, abstracts, and then full texts were screened against eligibility criteria, resulting in twenty studies being included. Critical appraisal was undertaken, and relevant data extracted and analysed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. RESULTS: Artificial intelligence was used in numerous areas including breast, colorectal, liver, and ovarian cancer care among others. Algorithms were trained and tested on primary and secondary datasets to build predictive models of health problems related to cancer. Studies reported this led to improvements in the accuracy of predicting health outcomes or identifying variables that improved outcome prediction. While nurses led most studies, few deployed an artificial intelligence based digital tool with cancer nurses in a real-world setting as studies largely focused on developing and validating predictive models. CONCLUSION: Electronic cancer nursing datasets should be established to enable artificial intelligence techniques to be tested and if effective implemented in digital prediction and other AI-based tools. Cancer nurses need more education on machine learning and natural language processing, so they can lead and contribute to artificial intelligence developments in oncology.