Keywords
Topic Modelling
Care Pathways
Nursing Leadership
Palliative Care
Digital Health
Nursing Research
Abstract
Background. Scientific congresses act as observatories of a professional community’s priorities and methods. The abstracts submitted to a national oncology nursing congress form a real-world corpus through which research and practice trajectories can be examined empirically rather than impressionistically.
Objective. To identify and interpret the emerging topics within the abstracts included in the AIIAO 2026 Congress proceedings using lexicometric analysis followed by topic modelling, and to discuss their implications for the oncology nursing agenda.
Methods. Exploratory, corpus-based text-mining study. The English structured bodies of the 82 included abstracts were preprocessed (lowercasing; removal of punctuation, digits and structural headings; lemmatization; moderate stopword control; n-gram detection). Lexicometric descriptives were computed, and Latent Dirichlet Allocation models were estimated for k = 4-7. The number of topics was selected by combining coherence with the CaoJuan2009, Arun2010 and Deveaud2014 criteria and interpretability, not by automatic optimisation alone.
Results. The corpus contained 21,347 running words and 3,568 unique word forms (type-token ratio 0.167; 44.6% hapax). A five-topic solution was selected: care pathways, procedural safety and quality improvement (35.4%); self-management, treatment adherence and nursing surveillance (14.6%); nursing research capacity and symptom-distress measurement (19.5%); professional roles, leadership and organizational/ethical development (17.1%); and palliative and supportive care, quality of life and continuity (13.4%). The latent topics cut across the author-assigned editorial clusters.
Conclusions. The topics suggest a maturing research community oriented towards safe, standardized and person-centred care, supported by advanced roles, outcome measurement and digital health. The findings describe the submitted corpus and are not representative of Italian oncology nursing as a whole.
References
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Basch, E., Schrag, D., Henson, S., et al. (2022). Effect of electronic symptom monitoring on patient-reported outcomes among patients with metastatic cancer: A randomized clinical trial. JAMA, 327(24), 2413–2422. https://doi.org/10.1001/jama.2022.9265
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Cao, J., Xia, T., Li, J., Zhang, Y., & Tang, S. (2009). A density-based method for adaptive LDA model selection. Neurocomputing, 72(7–9), 1775–1781. https://doi.org/10.1016/j.neucom.2008.06.011
Danler, M., Hackl, W. O., Neururer, S. B., Huber, L., & Pfeifer, B. (2024). Visualizing nursing narratives: An evaluation of latent Dirichlet allocation topic modeling for care reports. Studies in Health Technology and Informatics, 316, 1709–1713. https://doi.org/10.3233/SHTI240752
Deveaud, R., SanJuan, E., & Bellot, P. (2014). Accurate and effective latent concept modeling for ad hoc information retrieval. Document Numérique, 17(1), 61–84. https://doi.org/10.3166/DN.17.1.61-84
Dowling, M., Pape, E., Geese, F., et al. (2024). Advanced practice nursing titles and roles in cancer care: A scoping review. Seminars in Oncology Nursing, 40(3), Article 151627. https://doi.org/10.1016/j.soncn.2024.151627
Greer, J. A., Temel, J. S., El-Jawahri, A., et al. (2024). Telehealth vs in-person early palliative care for patients with advanced lung cancer: A multisite randomized clinical trial. JAMA, 332(14), 1153–1164. https://doi.org/10.1001/jama.2024.13964
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl. 1), 5228–5235. https://doi.org/10.1073/pnas.0307752101
Kocarnik, J. M., Compton, K., Dean, F. E., et al. (2022). Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: A systematic analysis for the Global Burden of Disease Study 2019. JAMA Oncology, 8(3), 420–444. https://doi.org/10.1001/jamaoncol.2021.6987
Oh, W. O., Lee, E., Heo, Y. J., Jung, M. J., & Han, J. (2024). Understanding global research trends in the control and prevention of infectious diseases for children: Insights from text mining and topic modeling. Journal of Nursing Scholarship, 56(4), 606–620. https://doi.org/10.1111/jnu.12963
Rosenzweig, M., Belcher, S. M., Braithwaite, L. E., et al. (2024). Research priorities of the Oncology Nursing Society: 2024–2027. Oncology Nursing Forum, 51(6), 502–515. https://doi.org/10.1188/24.ONF.502-515
Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM ’15) (pp. 399–408). Association for Computing Machinery. https://doi.org/10.1145/2684822.2685324
Schenker, Y., Althouse, A. D., Rosenzweig, M., et al. (2021). Effect of an oncology nurse-led primary palliative care intervention on patients with advanced cancer: The CONNECT cluster randomized clinical trial. JAMA Internal Medicine, 181(11), 1451–1460. https://doi.org/10.1001/jamainternmed.2021.5185
Shaffer, K. M., Turner, K. L., Siwik, C., et al. (2023). Digital health and telehealth in cancer care: A scoping review of reviews. The Lancet Digital Health, 5(5), e316–e327. https://doi.org/10.1016/S2589-7500(23)00049-3Arun, R., Suresh, V., Veni Madhavan, C. E., & Narasimha Murty, M. (2010). On finding the natural number of topics with latent Dirichlet allocation: Some observations. In M. J. Zaki, J. X. Yu, B. Ravindran, & V. Pudi (Eds.), Advances in knowledge discovery and data mining (Lecture Notes in Computer Science, Vol. 6118, pp. 391–402). Springer. https://doi.org/10.1007/978-3-642-13657-3_43
Basch, E., Schrag, D., Henson, S., et al. (2022). Effect of electronic symptom monitoring on patient-reported outcomes among patients with metastatic cancer: A randomized clinical trial. JAMA, 327(24), 2413–2422. https://doi.org/10.1001/jama.2022.9265
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Cao, J., Xia, T., Li, J., Zhang, Y., & Tang, S. (2009). A density-based method for adaptive LDA model selection. Neurocomputing, 72(7–9), 1775–1781. https://doi.org/10.1016/j.neucom.2008.06.011
Danler, M., Hackl, W. O., Neururer, S. B., Huber, L., & Pfeifer, B. (2024). Visualizing nursing narratives: An evaluation of latent Dirichlet allocation topic modeling for care reports. Studies in Health Technology and Informatics, 316, 1709–1713. https://doi.org/10.3233/SHTI240752
Deveaud, R., SanJuan, E., & Bellot, P. (2014). Accurate and effective latent concept modeling for ad hoc information retrieval. Document Numérique, 17(1), 61–84. https://doi.org/10.3166/DN.17.1.61-84
Dowling, M., Pape, E., Geese, F., et al. (2024). Advanced practice nursing titles and roles in cancer care: A scoping review. Seminars in Oncology Nursing, 40(3), Article 151627. https://doi.org/10.1016/j.soncn.2024.151627
Greer, J. A., Temel, J. S., El-Jawahri, A., et al. (2024). Telehealth vs in-person early palliative care for patients with advanced lung cancer: A multisite randomized clinical trial. JAMA, 332(14), 1153–1164. https://doi.org/10.1001/jama.2024.13964
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl. 1), 5228–5235. https://doi.org/10.1073/pnas.0307752101
Kocarnik, J. M., Compton, K., Dean, F. E., et al. (2022). Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: A systematic analysis for the Global Burden of Disease Study 2019. JAMA Oncology, 8(3), 420–444. https://doi.org/10.1001/jamaoncol.2021.6987
Oh, W. O., Lee, E., Heo, Y. J., Jung, M. J., & Han, J. (2024). Understanding global research trends in the control and prevention of infectious diseases for children: Insights from text mining and topic modeling. Journal of Nursing Scholarship, 56(4), 606–620. https://doi.org/10.1111/jnu.12963
Rosenzweig, M., Belcher, S. M., Braithwaite, L. E., et al. (2024). Research priorities of the Oncology Nursing Society: 2024–2027. Oncology Nursing Forum, 51(6), 502–515. https://doi.org/10.1188/24.ONF.502-515
Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM ’15) (pp. 399–408). Association for Computing Machinery. https://doi.org/10.1145/2684822.2685324
Schenker, Y., Althouse, A. D., Rosenzweig, M., et al. (2021). Effect of an oncology nurse-led primary palliative care intervention on patients with advanced cancer: The CONNECT cluster randomized clinical trial. JAMA Internal Medicine, 181(11), 1451–1460. https://doi.org/10.1001/jamainternmed.2021.5185
Shaffer, K. M., Turner, K. L., Siwik, C., et al. (2023). Digital health and telehealth in cancer care: A scoping review of reviews. The Lancet Digital Health, 5(5), e316–e327. https://doi.org/10.1016/S2589-7500(23)00049-3
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Copyright (c) 2026 Rosario Caruso, Maddalena De Maria, Gianluca Conte, Marco Alfredo Arcidiacono, Serena Caponetti, Gabriele Cremona, Marcella Dabbene, Monica Guberti, Alessio Piredda, Arianna Magon