October 2023 Texas Board of Nursing Bulletin

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Jan Hooper Elected to Second Term on the NCSBN Leadership Succession Committee The Texas Board of Nursing (BON or Board) is proud to announce Lead Nursing Education Consultant Dr. Janice Hooper was elected during the August 2023 gathering of the National Council of State Boards of Nursing (NCSBN) Delegate Assembly to serve a second term on the Leadership Succession Committee.

In 2002, Dr. Hooper returned to her native Texas and joined the Board staff as an Education Consultant and was appointed Lead Consultant in 2009. She was inducted into the NCSBN Fellowship of the Institute for Regulatory Excellence in 2012, served as Chair of the NCSBN NCLEX Examination Committee for five years, and chaired the NCSBN Education Outcomes and Metrics Committee for two years. Dr. Hooper is a Fellow in the Academy of Nursing Education (ANEF) and a Fellow in the American Academy of Nursing (FAAN). Further, she is certified by the National League for Nursing in nursing education. Dr. Hooper is a prolific nursing researcher and author. Her works are found in the NCSBN’s Journal of Nursing Regulation and Leader to Leader. Congratulations, Dr. Hooper!

Janice I. Hooper, PhD, RN, FRE, CNE, FAAN, ANEF, began her nursing career as an associate degree nursing graduate from Maryville University in St. Louis, Missouri. She holds a Baccalaureate Degree in Nursing, a Masters Degree in Nursing (MSN) in Nursing of Children, and a PhD from St. Louis University. As a lifelong learner, Dr. Hooper completed four additional master’s degrees: an MSN in Community Health from the University of Missouri-Columbia and Masters Degrees in Management, Early Childhood Development, and Counseling from Webster University in St. Louis. While engaged in these educational pursuits, Dr. Hooper served as a nursing program faculty member, program director, and department dean in the St. Louis area for over 20 years.

AI Tools in Nursing - cont. from prev. page cation of Sigma Theta Tau International Honor Society of Nursing, 53(6), 803–814. https://doi.org/10.1111/jnu.12711 National Council of State Boards of Nursing (NCSBN). (2014). Pencils down, booklets closed. In Focus. (Spring 2014, pp 10-13). National Council of State Boards of Nursing (NCSBN). (2019). The NCSBN 2019 environmental scan: 40th anniversary edition. Journal of Nursing Regulation, S1-S40, 9(4). https://doi: 10.1016/S21558256(18)30177-7 National Council of State Boards of Nursing (NCSBN). (2021). NCSBN’s environmental scan COVID-19 and its impact on nursing and regulation. Journal of Nursing Regulation, S1-S36, 11(4). https:// Doi:10.1016/S2155-8256(21)00002-8 National Council of State Boards of Nursing (NCSBN). (2023). The NCSBN 2023 environmental scan: Nursing at a crossroads--an opportunity for action. Journal of Nursing Regulation, S10-S48, 13(4). DOI: 10.1016/S2155-8256(23)00006-6 Pepito, J.A., and Locsin, R. (2019). Can nurses remain relevant in a technologically advanced future? International Journal of Nursing Sciences. 106-110. https:// DOI: 10.1016/j.ijnss.2018.09.013

Robert, N. How artificial intelligence is changing nursing. Nurs Manage. (2019) Sep;50(9):30-39. https://doi: 10.1097/01. NUMA.0000578988.56622.21. PMID: 31425440; PMCID: PMC7597764. Song, W., Kang, M. J., Zhang, L., Jung, W., Song, J., Bates, D. W., & Dykes, P. C. (2021). Predicting pressure injury using nursing assessment phenotypes and machine learning methods. Journal of the American Medical Informatics Association : JAMIA, 28(4), 759–765. https://doi. org/10.1093/jamia/ocaa336 Soriano, G.P., Yasuhara, Y., Ito, H., Matsumoto, K., Osaka, K., Kai, Y., Locsin, R., Schoenhofer, S., and Tanioka, T. (2022). Robots and robotics in nursing. Healthcare 2022, 10, 1571, 10(8). https://doi. org/10.3390/healthcare10081571 Thibault, G. E. (2020). The future of health professions education: Emerging trends in the United States. FASEB BioAdvances, 2(12), 685–694. https://doi.org/10.1096/ fba.2020-00061 U.S. Bureau of Labor Statistics. (2022). Occupational Employment Statistics. Retrieved from, http://www.bls.gov/oes/ oes_emp.htm van Wynsberghe, A. (2015). Healthcare robots: Ethics, design and implementa16

tion in emerging technologies. Ethics and International Affairs; Routledge: London, UK, 2015. ISBN 9781032098609 Vial, A. Stirling, D., Field, M., Ros, M., Ritz, C., Carolan, M. & Miller, A.A. (2018). The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Translational Cancer Research, 7(3), 803-816. von Gerich, H., Moen, H., Block, L., Chu, C., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., Olalia, M., Pruinelli, L., Ronquillo, C., Topaz, M., and Peltonen, L. (2022). Artificial intelligence-based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies. Mar;127:104153. https://doi: 10.1016/j. ijnurstu.2021.104153. Epub 2021 Dec 7. PMID: 35092870. Wakefield, K. (2023). Predictive modeling analytics and machine learning. SAS Institute. Retrieved from https:// www.sas.com/en_gb/insights/articles/ analytics/a-guide-to-predictive-analytics-and-machine-learning.html World Health Organization. (2021). Ethics and governance of artificial intelligence for health. WHO Guidance. https://apps. who.int/iris/handle/10665/341996. License: CC BY-NC-SA 3.0 IGO


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