AimTo explore how Italian nurses working in oncology perceive patient advocacy within their clinical environments.DesignAn observational, cross-sectional study examining self-perceptions of patient advocacy in oncology nursing practice.MethodsDemographic data were summarized as frequencies and percentages. Open-ended responses were analyzed using generative artificial intelligence (GAI). A freely accessible large language model (LLM) was employed to identify, cluster, and summarize the most relevant concepts and recurring terms provided by oncology nurses. The LLM enabled the extraction of key themes concerning experiences, strategies, and emotional outcomes related to cancer advocacy.ResultsA total of 183 Italian oncology nurses participated in the study. Most participants were female (78.1%); 60.1% held a bachelor's degree, 55.2% had completed postbasic training, and 25.1% possessed a specific oncology certificate. The analysis revealed core emotional and professional themes, highlighting nurses' sense of moral responsibility, communication challenges, and ethical dilemmas when advocating for patients.ConclusionsThe findings underscore the emotional and professional complexity inherent in oncology nursing advocacy. While many nurses reported empowerment and professional fulfillment through advocacy, others experienced frustration and isolation when encountering conflict or resistance.Implications for Nursing PracticeThis study emphasizes the need for structured education, emotional support, and communication training to better prepare oncology nurses for the demands of patient advocacy.

Vitale, E., Lupo, R., De Nunzio, G., Cascio, D., Botti, S., Maci, S., et al. (2025). A Statistical and Large Language Model to Define Oncology Nursing Advocacy Self‐Perceptions: An Explorative Study. NURSING FORUM, 2025(1) [10.1155/nuf/5423366].

A Statistical and Large Language Model to Define Oncology Nursing Advocacy Self‐Perceptions: An Explorative Study

Cascio, Donato;Conte, Luana
2025-12-18

Abstract

AimTo explore how Italian nurses working in oncology perceive patient advocacy within their clinical environments.DesignAn observational, cross-sectional study examining self-perceptions of patient advocacy in oncology nursing practice.MethodsDemographic data were summarized as frequencies and percentages. Open-ended responses were analyzed using generative artificial intelligence (GAI). A freely accessible large language model (LLM) was employed to identify, cluster, and summarize the most relevant concepts and recurring terms provided by oncology nurses. The LLM enabled the extraction of key themes concerning experiences, strategies, and emotional outcomes related to cancer advocacy.ResultsA total of 183 Italian oncology nurses participated in the study. Most participants were female (78.1%); 60.1% held a bachelor's degree, 55.2% had completed postbasic training, and 25.1% possessed a specific oncology certificate. The analysis revealed core emotional and professional themes, highlighting nurses' sense of moral responsibility, communication challenges, and ethical dilemmas when advocating for patients.ConclusionsThe findings underscore the emotional and professional complexity inherent in oncology nursing advocacy. While many nurses reported empowerment and professional fulfillment through advocacy, others experienced frustration and isolation when encountering conflict or resistance.Implications for Nursing PracticeThis study emphasizes the need for structured education, emotional support, and communication training to better prepare oncology nurses for the demands of patient advocacy.
18-dic-2025
Settore PHYS-06/A - Fisica per le scienze della vita, l'ambiente e i beni culturali
Vitale, E., Lupo, R., De Nunzio, G., Cascio, D., Botti, S., Maci, S., et al. (2025). A Statistical and Large Language Model to Define Oncology Nursing Advocacy Self‐Perceptions: An Explorative Study. NURSING FORUM, 2025(1) [10.1155/nuf/5423366].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/705223
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