Seminars in Oncology Nursing最新文献_第2页

Theoretical Perspectives on Patient and Caregiver Experiences After CAR T-Cell Therapy CAR - t细胞治疗后患者和护理者体验的理论观点。

IF 2.3 4区医学 Q1 NURSING

Seminars in Oncology Nursing

Pub Date : 2025-12-23 DOI: 10.1016/j.soncn.2025.152095

Iman Nurjaman, Blacius Dedi, Depi Rismayanti, Ade Fitriani, Rany Yulianie

引用次数: 0

Artificial Intelligence in Colorectal Cancer Supportive Care: A Scoping Review 人工智能在结直肠癌支持治疗中的应用综述

IF 2.3 4区医学 Q1 NURSING

Seminars in Oncology Nursing

Pub Date : 2025-12-20 DOI: 10.1016/j.soncn.2025.152079

Yupawadee Kantabanlang , Misun Hwang , John C. Krauss , Yun Jiang

Purpose

Artificial Intelligence (AI) has the potential to enhance supportive care for cancer survivors from diagnosis through treatment and into survivorship. This study aimed to provide an overview of available evidence on AI applications in supportive care for individuals with colorectal cancer (CRC).

Methods

This scoping review was conducted following the Joanna Briggs Institute guidelines. Studies published between 2014 and 2025 were retrieved from six databases: PubMed, Embase, CINAHL, Scopus, Web of Science, and PsycINFO, using a combination of search terms relevant to “colorectal cancer,” “artificial intelligence,” and “supportive care.” Data on study characteristics, participants, settings, types of AI technologies, and supportive care dimensions were extracted for analysis.

Results

Out of 1,792 articles, 40 were identified as eligible for inclusion in this scoping review. AI applications for CRC supportive care are primarily in early development, focusing on machine learning-based prediction models that provide informational support for post-surgical side effects. The use of AI for physical support in symptom management and emotional support during cancer treatment and beyond was limited.

Conclusion

Implementing AI technology offers an opportunity to enhance supportive care for patients with CRC. This study suggests that current AI applications for CRC supportive care primarily focus on informational support, underscoring the need for further development of AI to provide comprehensive support, including psychological, social, spiritual, and practical aspects.

Implications for Nursing Practice

Further research is needed to develop AI-driven tools that comprehensively address the supportive care needs of cancer patients and enhance their outcomes.

目的：人工智能（AI）有可能增强对癌症幸存者从诊断到治疗到生存的支持性护理。本研究旨在概述人工智能在结直肠癌（CRC）患者支持性护理中的应用。方法：根据乔安娜布里格斯研究所的指导方针进行范围审查。2014年至2025年间发表的研究从六个数据库中检索：PubMed、Embase、CINAHL、Scopus、Web of Science和PsycINFO，使用与“结直肠癌”、“人工智能”和“支持性护理”相关的搜索词组合。提取有关研究特征、参与者、设置、人工智能技术类型和支持性护理维度的数据进行分析。结果：在1792篇文章中，有40篇被确定为符合纳入本范围综述的条件。人工智能在CRC支持治疗中的应用主要处于早期开发阶段，重点是基于机器学习的预测模型，为术后副作用提供信息支持。在癌症治疗期间及其后，人工智能在症状管理和情感支持方面的物理支持的使用是有限的。结论：人工智能技术的实施为加强结直肠癌患者的支持性护理提供了机会。本研究表明，目前人工智能在结直肠癌支持治疗中的应用主要集中在信息支持上，需要进一步发展人工智能来提供包括心理、社会、精神和实践方面的综合支持。对护理实践的影响：需要进一步研究开发人工智能驱动的工具，以全面解决癌症患者的支持性护理需求并提高其结果。

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引用次数: 0

The Effect of Interpersonal Psychotherapy on Psychosocial Problems Among Breast Cancer Patients: A Systematic Review. 人际心理治疗对乳腺癌患者心理社会问题的影响：系统综述。

IF 2.3 4区医学 Q1 NURSING

Seminars in Oncology Nursing

Pub Date : 2025-12-19 DOI: 10.1016/j.soncn.2025.152080

Tuğba Şahin Tokatlıoğlu, Fahriye Oflaz

Objective: Breast cancer, one of the most prevalent cancers among women, not only results in significant physical changes but also causes considerable emotional distress and disruptions in social roles and relationships. While depression is the most commonly observed psychiatric disorder in cancer patients, there is a lack of sufficient research on the effectiveness, potential drug interactions, and side effects of antidepressants in this population. This gap underscores the increasing need for non-pharmacological approaches, such as psychotherapy. Therefore, the aim of this study was to assess the effectiveness of Interpersonal Psychotherapy (IPT) in addressing psychosocial challenges faced by breast cancer patients.

Methods: This systematic review was registered in the PROSPERO database (CRD42024576746). A systematic literature search was conducted across five databases: Cochrane Library, PubMed, Web of Science, Scopus, and Ovid MEDLINE. The review was carried out according to PRISMA guidelines. Study selection and data extraction were independently performed by two researchers using the Covidence platform. Disagreements were resolved through discussion to reach consensus.

Results: Four studies (two randomized controlled trials [RCTs], two pilots) met inclusion criteria. IPT significantly reduced depression, anxiety, and psychological distress, with some studies also reporting improvements in quality of life. One study showed strong effect sizes (Cohen's d > 1.0) for depression. Telephone-based IPT was feasible and beneficial. Findings support IPT as an effective psychosocial intervention for breast cancer patients.

Conclusion: IPT appears to be an effective intervention for reducing anxiety and depression and enhancing quality of life in patients with breast cancer. However, further high-quality, large-scale RCTs are needed to strengthen the evidence base and confirm its clinical utility.

目的：乳腺癌是女性中最常见的癌症之一，它不仅会导致显著的身体变化，还会导致相当大的情绪困扰和社会角色和人际关系的中断。虽然抑郁症是癌症患者中最常见的精神障碍，但对抗抑郁药在这一人群中的有效性、潜在的药物相互作用和副作用缺乏足够的研究。这一差距凸显了对心理治疗等非药物治疗方法日益增长的需求。因此，本研究的目的是评估人际心理治疗（IPT）在解决乳腺癌患者面临的社会心理挑战方面的有效性。方法：本系统评价在PROSPERO数据库注册（CRD42024576746）。系统地检索了5个数据库：Cochrane Library、PubMed、Web of Science、Scopus和Ovid MEDLINE。审查是根据PRISMA的指导方针进行的。研究选择和数据提取由两名研究人员使用covid平台独立进行。通过讨论解决分歧，达成共识。结果：4项研究（2项随机对照试验[rct]， 2名试点）符合纳入标准。IPT显著减少了抑郁、焦虑和心理困扰，一些研究还报告了生活质量的改善。一项研究显示抑郁症有很强的效应量（Cohen’s d bbb1.0）。基于电话的IPT是可行和有益的。研究结果支持IPT作为一种有效的乳腺癌患者心理社会干预手段。结论：IPT似乎是一种有效的干预措施，可以减少乳腺癌患者的焦虑和抑郁，提高生活质量。然而，需要进一步的高质量、大规模的随机对照试验来加强证据基础并确认其临床应用。

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引用次数: 0

Exercise During or After Intravesical Therapy for Bladder Cancer: A Randomized Feasibility Trial 膀胱癌膀胱内治疗期间或之后的运动：一项随机可行性试验。

IF 2.3 4区医学 Q1 NURSING

Seminars in Oncology Nursing

Pub Date : 2025-12-18 DOI: 10.1016/j.soncn.2025.152090

Fernanda Z. Arthuso , Adrian S. Fairey , Normand G. Boulé , Niels-Erik Jacobsen , Lucas W. Dean , Kerry S. Courneya

Objectives

About 75% of newly diagnosed bladder cancers are non-muscle invasive bladder cancer (NMIBC). NMIBC and its treatments affect patient functioning and quality of life. Exercise is feasible, safe, and beneficial for many cancer patient groups, however, no studies have examined exercise for NMIBC. We aimed to examine the feasibility, safety, and preliminary efficacy of high-intensity interval training (HIIT) for patients with NMIBC during or after intravesical therapy.

Methods

The Bladder cancer and exeRcise trAining during or after intraVesical thErapy (BRAVE) trial randomized 25 NMIBC patients scheduled for or on surveillance after intravesical therapy to either usual care (n = 12) or HIIT (n = 13). The HIIT group performed thrice-weekly, supervised HIIT for 12 weeks.

Results

In 39 months, 293 patients were screened, 177 (60.4%) were eligible, and 25 (14.1%) were randomized. Median exercise attendance was 100%. From baseline to 12 weeks, VO_2peak increased by 1.2 mL/kg/min in the HIIT group compared to a decrease of 0.7 mL/kg/min in the usual care group (adjusted between-group difference, 2.0 mL/kg/min; 95% CI: −0.4 to 4.4; P = .10; d = 0.37). Compared to the usual care group at 12 weeks, the HIIT group significantly improved 6-minute walk distance (adjusted between-group difference, 41 meters; 95% CI: 6-77; P = .025; d = 0.32) and the timed 8-foot up-and-go (adjusted between-group difference, −1.0 second; 95% CI: −1.9 to −0.2; P = .019; d = −0.44).

Conclusions

Despite modest accrual, the BRAVE trial demonstrated that HIIT during or after intravesical therapy was safe and feasible for most NMIBC patients and resulted in meaningful improvements in several indicators of physical functioning.

Implications for Nursing Practice

Oncology nurses can inform NMIBC patients that high-intensity interval training may be safe, feasible, and potentially effective in improving physical functioning during or after intravesical therapy.

目的：约75%的新诊断膀胱癌为非肌性浸润性膀胱癌（NMIBC）。NMIBC及其治疗影响患者的功能和生活质量。然而，对于许多癌症患者群体来说，锻炼是可行的、安全的、有益的，没有研究对NMIBC进行过锻炼。我们的目的是研究高强度间歇训练（HIIT）在NMIBC患者膀胱治疗期间或之后的可行性、安全性和初步疗效。方法：膀胱内治疗期间或之后的膀胱癌和运动训练（BRAVE）试验随机选择25例膀胱内治疗后计划或接受监测的NMIBC患者进行常规护理（n = 12）或HIIT （n = 13）。HIIT组每周进行三次，监督HIIT 12周。结果：39个月，筛查293例患者，符合条件177例（60.4%），随机25例（14.1%）。运动出勤率中位数为100%。从基线到12周，HIIT组的VO2peak增加了1.2 mL/kg/min，而常规护理组的VO2peak减少了0.7 mL/kg/min（调整后组间差异为2.0 mL/kg/min; 95% CI: -0.4 ~ 4.4; P = 0.10; d = 0.37）。与常规护理组相比，HIIT组在12周时显著改善了6分钟步行距离（调整后的组间差异为41米；95% CI: 6-77; P = 0.025; d = 0.32）和8英尺起跑时间（调整后的组间差异为-1.0秒；95% CI: -1.9至-0.2;P = 0.019; d = -0.44）。结论：尽管有适度的累积，但BRAVE试验表明，HIIT在膀胱内治疗期间或之后对大多数NMIBC患者是安全可行的，并导致身体功能的几个指标有意义的改善。对护理实践的启示：肿瘤学护士可以告知NMIBC患者，高强度间歇训练可能是安全、可行的，并且在膀胱内治疗期间或之后改善身体功能可能有效。

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引用次数: 0

Development of Digital Literacy: Application of Artificial Intelligence in Education and Clinical Practice of Oncology Nurses 数字素养的发展：人工智能在肿瘤护士教育和临床实践中的应用。

IF 2.3 4区医学 Q1 NURSING

Seminars in Oncology Nursing

Pub Date : 2025-12-18 DOI: 10.1016/j.soncn.2025.152062

Nevena Šimunić , Nikolina Višnjić Junaković , Marko Skelin

Objectives

To synthesize current educational approaches to AI literacy in oncology nursing, identify key competency domains along with barriers and enablers, and offer clinically oriented recommendations for the safe and effective integration of AI into clinical practice.

Methods

Structured search of MEDLINE via PubMed (2015-2025) using MeSH and free-text terms, complemented with free sources (Google Scholar, OpenAlex/Lens), handsearching of key journals, and backward/forward citation chasing. Study selection was performed by two independent reviewers, with disagreements resolved by consultation with a third author; the process is summarized in a PRISMA 2020 flow diagram.

Results

Findings confirm that digital and AI literacy are fundamental for oncology nurses. Effective use of AI requires a grasp of basic ML principles, data interpretation, and ethics. Educational strategies include integration into formal curricula and innovative formats such as microlearning, simulations, and virtual reality. Key barriers are uneven digital skills, resistance to technology, and lack of structured programs. Successful education is further supported by multidisciplinary collaboration and patient involvement. Evidence suggests that AI enhances clinical decision-making, personalized care, safety, and nurse autonomy.

Conclusions

Incorporating AI competencies into nursing education is crucial for improving safety and quality in oncology care. Educational reforms should foster critical thinking, ensure ongoing evaluation, and preserve empathy towards patients. Verified and flexible programs enable sustainable literacy development aligned with technological and ethical standards.

Implications for Nursing Practice

Nurses educated in AI can improve clinical decision-making, reduce errors, and provide empathetic, individualized care. AI should be regarded solely as a tool that supports nurses’ work, not as a replacement. Interdisciplinary and patient-centered approaches support the safe integration of AI into daily oncology nursing practice. This review uniquely focuses on oncology nursing, integrates peer-reviewed and professional/grey sources, and offers practical curriculum and clinical integration recommendations that complement recent reviews.

目的：综合目前肿瘤护理中人工智能素养的教育方法，确定关键能力领域以及障碍和推动因素，并为安全有效地将人工智能整合到临床实践中提供临床导向的建议。方法：通过PubMed（2015-2025）使用MeSH和free-text术语对MEDLINE进行结构化检索，并辅以免费来源（谷歌Scholar、OpenAlex/Lens），手工检索关键期刊，进行逆向/正向引文追踪。研究选择由两名独立的审稿人进行，分歧通过与第三作者协商解决；该过程在PRISMA 2020流程图中进行了总结。结果：研究结果证实，数字和人工智能素养是肿瘤科护士的基础。有效地使用人工智能需要掌握基本的机器学习原理、数据解释和道德规范。教育策略包括融入正式课程和创新形式，如微学习、模拟和虚拟现实。主要障碍是数字技能参差不齐、对技术的抵制以及缺乏结构化的项目。多学科合作和患者参与进一步支持成功的教育。有证据表明，人工智能可以增强临床决策、个性化护理、安全性和护士自主权。结论：将人工智能能力纳入护理教育对提高肿瘤护理的安全性和质量至关重要。教育改革应培养批判性思维，确保持续的评估，并保持对患者的同情。经过验证的灵活方案可使扫盲发展符合技术和道德标准。对护理实践的影响：接受人工智能教育的护士可以改善临床决策，减少错误，并提供同理心和个性化的护理。人工智能应该仅仅被视为支持护士工作的工具，而不是替代品。跨学科和以患者为中心的方法支持将人工智能安全整合到日常肿瘤护理实践中。本综述独特地关注肿瘤护理，整合了同行评审和专业/灰色资源，并提供了实用的课程和临床整合建议，以补充最近的综述。

{"title":"Development of Digital Literacy: Application of Artificial Intelligence in Education and Clinical Practice of Oncology Nurses","authors":"Nevena Šimunić , Nikolina Višnjić Junaković , Marko Skelin","doi":"10.1016/j.soncn.2025.152062","DOIUrl":"10.1016/j.soncn.2025.152062","url":null,"abstract":"<div><h3>Objectives</h3><div>To synthesize current educational approaches to AI literacy in oncology nursing, identify key competency domains along with barriers and enablers, and offer clinically oriented recommendations for the safe and effective integration of AI into clinical practice.</div></div><div><h3>Methods</h3><div>Structured search of MEDLINE via PubMed (2015-2025) using MeSH and free-text terms, complemented with free sources (Google Scholar, OpenAlex/Lens), handsearching of key journals, and backward/forward citation chasing. Study selection was performed by two independent reviewers, with disagreements resolved by consultation with a third author; the process is summarized in a PRISMA 2020 flow diagram.</div></div><div><h3>Results</h3><div>Findings confirm that digital and AI literacy are fundamental for oncology nurses. Effective use of AI requires a grasp of basic ML principles, data interpretation, and ethics. Educational strategies include integration into formal curricula and innovative formats such as microlearning, simulations, and virtual reality. Key barriers are uneven digital skills, resistance to technology, and lack of structured programs. Successful education is further supported by multidisciplinary collaboration and patient involvement. Evidence suggests that AI enhances clinical decision-making, personalized care, safety, and nurse autonomy.</div></div><div><h3>Conclusions</h3><div>Incorporating AI competencies into nursing education is crucial for improving safety and quality in oncology care. Educational reforms should foster critical thinking, ensure ongoing evaluation, and preserve empathy towards patients. Verified and flexible programs enable sustainable literacy development aligned with technological and ethical standards.</div></div><div><h3>Implications for Nursing Practice</h3><div>Nurses educated in AI can improve clinical decision-making, reduce errors, and provide empathetic, individualized care. AI should be regarded solely as a tool that supports nurses’ work, not as a replacement. Interdisciplinary and patient-centered approaches support the safe integration of AI into daily oncology nursing practice. This review uniquely focuses on oncology nursing, integrates peer-reviewed and professional/grey sources, and offers practical curriculum and clinical integration recommendations that complement recent reviews.</div></div>","PeriodicalId":54253,"journal":{"name":"Seminars in Oncology Nursing","volume":"42 1","pages":"Article 152062"},"PeriodicalIF":2.3,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145795295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence in Pediatric Oncology Care: Pediatric Nurses’ Perspectives and Future Implications 人工智能在儿科肿瘤护理中的应用：儿科护士的观点和未来意义。

IF 2.3 4区医学 Q1 NURSING

Seminars in Oncology Nursing

Pub Date : 2025-12-15 DOI: 10.1016/j.soncn.2025.152082

Sevil Çınar Özbay , Dilek Gelin , Selma Durmuş Sarıkahya

Objectives

This study aimed to explore pediatric oncology nurses’ perspectives on the integration of artificial intelligence (AI) into pediatric oncology care, focusing on its potential advantages, implementation challenges, and ethical considerations.

Methods

A hermeneutically informed descriptive phenomenological qualitative design was employed. One-to-one, semistructured interviews were conducted with 18 pediatric oncology nurses between April and June 2025. All interviews were audio-recorded, transcribed verbatim, and analyzed thematically in MAXQDA using Braun and Clarke’s six-phase framework (2006). Reporting followed the COREQ checklist.

Results

Participants’ mean age was 36.8 ± 6.9 years, and most had >10 years of professional experience. Thematic analysis identified five main themes with related subthemes: (1) the potential of AI in pediatric oncology care; (2) implementation challenges and concerns; (3) nurse–AI collaboration; (4) ethical considerations; and (5) competence and training needs. Nurses highlighted AI’s potential to accelerate diagnostic/treatment processes, reduce error, and enhance patient safety, while also noting barriers related to infrastructure, ethics, and professional skills.

Conclusions

Pediatric oncology nurses perceived AI as a valuable tool to support clinical decision-making, improve patient safety, and increase care efficiency; however, ethical concerns, infrastructural limitations, and insufficient training constrain effective integration.

Implications for Nursing Practice

Strengthening technological competencies, ensuring ethical safeguards, and providing continuous training are essential for successful AI integration. Combining clinical expertise with AI competence may promote safer, more effective pediatric oncology care.

目的：本研究旨在探讨儿科肿瘤护士对人工智能（AI）融入儿科肿瘤护理的看法，重点关注其潜在优势、实施挑战和伦理考虑。方法：采用解释学信息描述现象学定性设计。本研究于2025年4月至6月对18名儿科肿瘤科护士进行了一对一的半结构化访谈。所有访谈都被录音，逐字转录，并在MAXQDA中使用Braun和Clarke的六阶段框架（2006）进行主题分析。报告遵循COREQ检查表。结果：参与者的平均年龄为36.8±6.9岁，大多数具有10年以上的工作经验。主题分析确定了五个主要主题和相关的副主题：(1)人工智能在儿科肿瘤护理中的潜力；(2)实施的挑战和关注；(3)护士与人工智能协作；(4)伦理考虑；(5)能力和培训需求。护士们强调了人工智能在加速诊断/治疗过程、减少错误和提高患者安全方面的潜力，同时也指出了与基础设施、道德和专业技能相关的障碍。结论：儿科肿瘤学护士认为人工智能是支持临床决策、改善患者安全和提高护理效率的宝贵工具；然而，伦理问题、基础设施限制和培训不足限制了有效的整合。对护理实践的影响：加强技术能力、确保道德保障和提供持续培训是成功整合人工智能的关键。将临床专业知识与人工智能能力相结合，可以促进更安全、更有效的儿科肿瘤护理。

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引用次数: 0

Ruminative Thoughts in Oncology Nursing: A Qualitative Inquiry into Their Nature, Effects, and Coping Strategies. 肿瘤护理中的反刍思维：性质、效果及应对策略的定性探讨。

IF 2.3 4区医学 Q1 NURSING

Seminars in Oncology Nursing

Pub Date : 2025-12-12 DOI: 10.1016/j.soncn.2025.152081

Sinem Öcalan, Yeter Sinem Üzar-Özçetin

Objectives: The aim of this study was to explore the ruminative thoughts experienced by oncology nurses, examine their impact on both personal and professional life, and identify the coping strategies employed in the context of cancer care.

Methods: This inductive qualitative study employed in-depth interviews with 20 oncology nurses, conducted between March and July 2021, and utilized content analysis for data analyze.

Results: The main theme identified was Ruminative thoughts and coping strategies of oncology nurses. It highlighted how oncology nurses' ruminative thoughts, driven by patient losses, fears of inadequacy, and cancer-related anxieties, affect their mental well-being and personal growth. The main theme is built upon the subthemes "Confronting professional and personal realities," "Redefining self and values," and "Overcoming strategies."

Conclusions: This study reveals that oncology nurses' ruminative thoughts, driven by patient deaths, treatment inadequacy, and cancer fears, lead to mental fatigue but also foster personal growth. Coping strategies like distraction, thought suppression, and adopting a commitment to excellence mindset help them manage these challenges.

Implications for nursing practice: Awareness of ruminative thoughts and the implementation of interventions such as resilience training, mindfulness-based practices, and team-based care approaches can reduce mental fatigue among nurses, enhancing their well-being and the quality of care they provide.

目的：本研究的目的是探讨肿瘤护士所经历的反刍思想，考察其对个人和职业生活的影响，并确定在癌症护理背景下采用的应对策略。方法：采用归纳性质的研究方法，于2021年3月至7月对20名肿瘤科护士进行深度访谈，采用内容分析法进行数据分析。结果：本次调查的主题为肿瘤科护士的反思思维及应对策略。它强调了肿瘤科护士在失去病人、对能力不足的恐惧以及与癌症相关的焦虑的驱使下，如何进行反刍思考，影响她们的心理健康和个人成长。主题是建立在“面对职业和个人现实”、“重新定义自我和价值观”和“克服策略”的次主题之上的。结论：本研究揭示了肿瘤护士在病人死亡、治疗不足、癌症恐惧等因素的驱使下产生的反刍思维，导致了精神疲劳，但也促进了个人成长。分散注意力、抑制思想、追求卓越的心态等应对策略有助于他们应对这些挑战。对护理实践的启示：意识到反刍思想和实施干预措施，如弹性训练、正念实践和团队护理方法，可以减少护士的精神疲劳，提高他们的幸福感和护理质量。

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引用次数: 0

Predictive Modeling of Cancer Information Overload and Screening Attitudes: A Cross-Sectional Study 癌症信息超载与筛查态度的预测模型：一项横断面研究。

IF 2.3 4区医学 Q1 NURSING

Seminars in Oncology Nursing

Pub Date : 2025-12-12 DOI: 10.1016/j.soncn.2025.152060

Nebıha Kenar , Funda Akduran

Objective

To explore the association between cancer information overload and attitudes toward cancer screening among internal medicine patients.

Aims

To identify predictors of screening attitudes using statistical and machine learning models.

Methods

A cross-sectional study was conducted with 410 internal medicine outpatients. Data were collected using the Cancer Information Overload Scale and the Attitude Scale for Cancer Screening. Analyses included t-tests, ANOVA, and regression. Seven machine learning models (KNN, SVM, ANN, RF, XGBoost, CART, Elastic Net) were compared using 10-fold cross-validation (R², RMSE, MAE). Statistical significance was set at P < .05.

Results

Participants’ mean age was 38.25 ± 12.46 years; 69.8% were female. Mean information overload and attitude scores were 17.14 ± 4.91 and 100.13 ± 13.58, respectively. Regression analysis showed a significant negative association between information overload and screening attitudes (β = −0.321, P < .001; R² = 0.103; 95% CI [−1.144, −0.634]). Among machine learning models, Elastic Net Regression (α = 0.2, λ = 1) achieved the best performance (RMSE = 12.6, MAE = 10.8), confirming cancer information overload as the strongest predictor.

Conclusion

Cancer information overload is inversely associated with screening attitudes. Machine learning models enhance interpretability, emphasizing the importance of managing information burden to improve cancer screening engagement.

目的：探讨内科患者癌症信息超载与癌症筛查态度的关系。目的：利用统计和机器学习模型确定筛选态度的预测因子。方法：对410例内科门诊患者进行横断面研究。使用癌症信息超载量表和癌症筛查态度量表收集数据。分析包括t检验、方差分析和回归。7种机器学习模型（KNN、SVM、ANN、RF、XGBoost、CART、Elastic Net）采用10倍交叉验证（R²、RMSE、MAE）进行比较。差异有统计学意义，P < 0.05。结果：参与者平均年龄38.25±12.46岁；69.8%为女性。平均信息过载和态度得分分别为17.14±4.91分和100.13±13.58分。回归分析显示，信息超载与筛查态度呈显著负相关（β = -0.321, P < .001; R²= 0.103;95% CI[-1.144, -0.634]）。在机器学习模型中，Elastic Net Regression （α = 0.2, λ = 1）的表现最好（RMSE = 12.6, MAE = 10.8），证实了癌症信息过载是最强的预测因子。结论：癌症信息超载与筛查态度呈负相关。机器学习模型增强了可解释性，强调了管理信息负担以提高癌症筛查参与度的重要性。

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引用次数: 0

Chemo Chair Conversations: A Qualitative Study of How Life and Death Influence Oncology Nurses’ Well-being and Professional Care 化疗椅对话：如何生与死影响肿瘤护士的福祉和专业护理的定性研究。

IF 2.3 4区医学 Q1 NURSING

Seminars in Oncology Nursing

Pub Date : 2025-12-06 DOI: 10.1016/j.soncn.2025.152061

Carolyn S. Phillips , Sue E. Morris , Cara C. Young , Megan C. Thomas Hebdon , Alfonzo Robinson , Catherine Bailey , Megan Lippe , Andra Davis

Objectives

Oncology nurses provide relational care with patients and families that require high levels of skill and empathy. This emotionally demanding work can lead to compassion fatigue, burnout, and unprocessed grief. Strategies to support oncology nurses are crucial for maintaining their well-being and delivering high-quality care. The purpose of this study was to analyze stories written by oncology nurses to understand the emotional experiences of caring for people with cancer.

Methods

A secondary qualitative analysis of 35 oncology nurses’ stories was conducted using Braun and Clarke’s thematic analysis framework. The nurses were participants in two Storytelling Through Music intervention studies, which included writing stories to process work-related emotions. Themes were developed to identify patterns and shared experiences across the narratives.

Results

The meta-theme of “Seeking Emotional Balance” emerged and was interwoven throughout the six themes: emotional labor, above and beyond, connections and mutual healing, cumulative grief and loss, coping and remembrance, and finding meaning. Nurses described the challenges of maintaining emotional balance while navigating professional and personal emotional demands.

Conclusion

Oncology nurses face unique relational and emotional challenges. While some found resilience in patient connections, others experienced chronic distress and burnout.

Storytelling provides a reflective outlet to process emotions, strengthen resilience, and foster shared understanding among peers. Storytelling interventions show promise as tools for emotional regulation and professional sustainability.

Implications for Nursing

Deep nurse–patient connections foster meaning and resilience but can blur boundaries, increasing risks like countertransference. Reflective practices help safeguard nurses’ well-being and care quality. At the individual level, nurses should adopt self-care strategies and engage in reflective practices. Organizational support is vital. Institutions can provide emotional resilience training, implement bereavement overload policies, and offer group storytelling opportunities to reduce stress, enhance regulation, and build supportive peer connections.

目的：肿瘤护士为患者和家属提供关系护理，这需要高水平的技能和同理心。这种需要情感的工作可能会导致同情疲劳、倦怠和未经处理的悲伤。支持肿瘤护士的策略对于维持她们的健康和提供高质量的护理至关重要。本研究的目的是分析肿瘤护士所写的故事，以了解护理癌症患者的情感体验。方法：采用Braun和Clarke的主题分析框架对35名肿瘤科护士的故事进行二次定性分析。护士们参加了两项通过音乐讲故事的干预研究，其中包括写故事来处理与工作有关的情绪。开发主题是为了确定叙事中的模式和共享经验。结果：“寻求情绪平衡”的元主题出现并交织在六个主题中：情绪劳动、超越与超越、联系与相互治疗、累积悲伤与失落、应对与记忆、寻找意义。护士们描述了在应对专业和个人情感需求的同时保持情绪平衡的挑战。结论：肿瘤科护士面临着独特的关系和情感挑战。一些人在与病人的交往中发现了韧性，而另一些人则经历了长期的痛苦和倦怠。讲故事为处理情绪、增强韧性和促进同伴之间的共同理解提供了一个反思的出口。讲故事干预有望成为情绪调节和职业可持续性的工具。对护理的影响：深厚的护患关系可以培养意义和恢复力，但也会模糊界限，增加反移情等风险。反思性做法有助于保障护士的福祉和护理质量。在个人层面上，护士应采取自我护理策略并参与反思实践。组织的支持是至关重要的。机构可以提供情绪恢复力培训，实施丧亲超载政策，并提供小组讲故事的机会，以减轻压力，加强监管，并建立支持性的同伴关系。

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引用次数: 0

Developing and Validating a Prediction Model for the Severe Pain-Fatigue-Sleep Disturbance Symptom Cluster in Patients with Lung Cancer Following Chemotherapy: A Machine Learning Analysis 建立和验证肺癌患者化疗后严重疼痛-疲劳-睡眠障碍症状群的预测模型：机器学习分析。

IF 2.3 4区医学 Q1 NURSING

Seminars in Oncology Nursing

Pub Date : 2025-12-05 DOI: 10.1016/j.soncn.2025.152063

Liping Teng , Zhou Zhou , Yiting Yang , Yajun Dong , Jun Sun , Teng Wang

Objectives

Pain-fatigue-sleep disturbance symptom (PFS) cluster is the most common symptom cluster in patients with lung cancer following chemotherapy, which significantly impacts their quality of life. This study aims to develop and validate a machine learning-based prediction model for the severe PFS cluster and identify the relevant factors in patients with lung cancer following chemotherapy.

Methods

A total of 612 patients were enrolled in the study, and logistic regression, along with four machine learning algorithms, was used. The area under the curve (AUC), accuracy, sensitivity, specificity, and Brier score were utilized for model evaluation. The Shapley additive interpretation and restricted cubic splines were employed to assess the significance of feature coefficients. A web-based application was developed to facilitate the practical implementation of the best model in clinical settings.

Results

The random forest model was identified as optimal, exhibiting the best discrimination and calibration in the test set (AUC: 0.765 and Brier score: 0.159) and excellent performance in the validation set (AUC: 0.914 and Brier score: 0.124). The factors encompassed in the model construction comprised stress, C-reactive protein, depression, body mass index (BMI), anxiety, neutrophils, age, gender, pathological classification, and Eastern Cooperative Oncology Group performance status. A nonlinear relationship existed between stress, BMI, age, and the severe PFS cluster.

Conclusions

The developed web program would assist health care professionals in accurately identifying patients experiencing the severe PFS cluster in clinical practice and facilitating efficient symptom management.

Implications for Nursing Practice

Clinical nurses can use a web-based calculator developed in this study to effectively identify patients with the severe PFS cluster and provide targeted interventions.

目的：疼痛-疲劳-睡眠障碍症状（PFS）是肺癌化疗后患者最常见的症状，显著影响患者的生活质量。本研究旨在开发和验证基于机器学习的重度PFS集群预测模型，并确定肺癌化疗后患者的相关因素。方法：共纳入612例患者，采用逻辑回归和4种机器学习算法。采用曲线下面积（AUC）、准确性、敏感性、特异性和Brier评分对模型进行评价。采用Shapley加性解释和限制三次样条来评估特征系数的显著性。开发了一个基于网络的应用程序，以促进临床环境中最佳模型的实际实施。结果：随机森林模型在测试集（AUC: 0.765， Brier评分：0.159）和验证集（AUC: 0.914， Brier评分：0.124）中具有最佳的识别和校准效果。模型构建的因素包括应激、c反应蛋白、抑郁、体重指数（BMI）、焦虑、中性粒细胞、年龄、性别、病理分型、东部肿瘤合作组成绩状况。应激、BMI、年龄与严重PFS聚类之间存在非线性关系。结论：开发的网络程序可以帮助医疗保健专业人员在临床实践中准确识别严重PFS群集的患者，并促进有效的症状管理。对护理实践的启示：临床护士可以使用本研究开发的基于网络的计算器来有效识别严重PFS患者，并提供有针对性的干预措施。

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引用次数: 0