Journal of Nursing Scholarship最新文献_第5页

Association between nurses’ personal, professional and work characteristics, and engagement in hospital-based clinical research 护士的个人、专业和工作特点与参与医院临床研究之间的关系。

IF 2.4 3区医学 Q1 NURSING

Journal of Nursing Scholarship

Pub Date : 2024-07-31 DOI: 10.1111/jnu.13010

Jennifer Colwill DNP, APRN, CCNS, PCCN, Heather Condo DiCioccio DNP, RNC-MNN, C-ONQS, James F. Bena MS, Shannon L. Morrison MS, Ashley Hall MSN, RN, CMSRN, Visnja Masina DNP, APRN, AGCNS-BC, Robon Vanek MA, MSN, APRN, Nancy M. Albert PhD, CCNS, NE-BC, FAAN

Purpose

The purpose of this study was to assess the associations between demographic, professional and other personal nurse characteristics, social support factors and comfort in conducting research with nurses' level of active participation in clinical research.

Design

A prospective, cross-sectional, correlational design was used.

Methods

Clinical nurses working in a multihospital healthcare system were recruited by email to complete an anonymous survey that used multiple valid and reliable scales to assess demographic and professional work characteristics, curiosity, grit, locus of control, perceived social support (for research activities), comfort in conducting research, and level of being research-active. Univariate and multivariable analyses were completed.

Results

Of 310 participants, 274 (88.4%) were female and mean (SD) age was 42.9 (13.1) years. After condensing 11 levels of research activity to four categories, 179 (57.7%) were not research-active, and 91 (29.4%), 26 (8.3%) and 14 (4.5%) were engaged at low, moderate, and high levels, respectively. Of 78 factors, 69 (88.5%) were associated with being research-active in univariate analyses. In multivariable analysis that adjusted for age, personal experience as a patient, years as a nurse and hours in direct patient care, professionalism characteristics, higher curiosity, internal locus of control, grit perseverance, support of a nurse scientist and nurse friends, and comfort in conducting research remained associated with higher levels of being research-active (all p < 0.01).

Conclusion

Research-active nurses were more likely to be engaged professionally in hospital-based activities beyond their work roles and displayed higher levels of positive psychological characteristics and mentorship that supported research capacity.

Clinical Relevance

Research-active nurses were more likely to have internal factors and external resources that promoted higher levels of being research-active. A strong professional governance model may enhance clinical nurses research activities.

目的：本研究旨在评估护士的人口统计学特征、职业特征和其他个人特征、社会支持因素以及开展研究的舒适度与护士积极参与临床研究的程度之间的关联：设计：采用前瞻性、横断面、相关性设计：通过电子邮件招募在一家多医院医疗系统工作的临床护士完成匿名调查，该调查采用多个有效可靠的量表来评估人口统计学和专业工作特征、好奇心、勇气、控制感、感知到的社会支持（对研究活动的支持）、开展研究的舒适度以及积极参与研究的程度。我们完成了单变量和多变量分析：在 310 名参与者中，274 人（88.4%）为女性，平均年龄为 42.9（13.1）岁。将研究活动的 11 个等级分为四类后，179 人（57.7%）不从事研究活动，91 人（29.4%）、26 人（8.3%）和 14 人（4.5%）分别从事低、中和高水平的研究活动。在单变量分析中，78 个因素中有 69 个（88.5%）与研究活跃度相关。在调整了年龄、作为病人的个人经历、作为护士的年限和直接护理病人的小时数、专业精神特征、较高的好奇心、内部控制感、坚韧不拔的精神、科学家护士和护士朋友的支持以及开展研究的舒适度等因素后进行的多变量分析中，较高的研究积极性仍然与较高的研究积极性相关（均为 p）：积极从事研究的护士更有可能以专业身份参与其工作角色之外的医院活动，并表现出更高水平的积极心理特征和支持研究能力的指导：临床相关性：积极从事科研的护士更有可能拥有促进其积极从事科研的内部因素和外部资源。强有力的专业管理模式可促进临床护士的研究活动。

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引用次数: 0

Can digital leadership transform AI anxiety and attitude in nurses? 数字化领导力能否改变护士对人工智能的焦虑和态度？

IF 2.4 3区医学 Q1 NURSING

Journal of Nursing Scholarship

Pub Date : 2024-07-31 DOI: 10.1111/jnu.13008

Sinan Tarsuslu PhD, Ferhat Onur Agaoglu PhD, Murat Bas PhD

<div> <section> <h3> Background</h3> <p>The lack of artificial intelligence applications in nursing education and the nursing profession in Turkey and the need for strategies for integrating artificial intelligence into the nursing profession continues. At this point, there is a need to transform the negative attitudes and anxiety that may occur in nurses.</p> </section> <section> <h3> Objectives</h3> <p>It was aimed to reorganize the professional transformation in this parallel by analyzing the effect of digital leadership perception, which is explained as how nurses approach digital technologies and innovations and their awareness of how and with which methods they can use these technologies on artificial intelligence anxiety and attitude in the nursing profession.</p> </section> <section> <h3> Design</h3> <p>The study was designed as descriptive, correlational, and cross-sectional.</p> </section> <section> <h3> Participants</h3> <p>The research was conducted by reaching 439 nurses working in hospitals operating in three different regions of Turkey by simple random sampling method.</p> </section> <section> <h3> Methods</h3> <p>In the first part of the data collection tool used in this study, digital leadership scale, artificial intelligence use anxiety, and artificial intelligence attitude scales were used, including questions determining the demographic information of nurses, their relationship with technology, artificial intelligence usage status and its importance in the profession.</p> </section> <section> <h3> Results</h3> <p>It was determined that 29.8% of the nurses had a good relationship with technology, 66.3% knew about using artificial intelligence in health, and 27.3% wanted it to be more involved in their lives. It was determined that nurses' perceptions of digital leadership were at a medium level of 46.9% and a high level of 41.7%, 82.7% had a positive attitude towards artificial intelligence, and 82.7% had low or medium level anxiety when their artificial intelligence anxiety status was examined. There was a significant and negative relationship between digital leadership and AI anxiety (<i>r</i> = −0.434; <i>p</i> < 0.01), a significant and positive relationship between digital leadership and AI attitude (<i>r</i> = 0.468; <i>p</i> < 0.01), and a significant and negative relationship between AI attitude and AI anxiety (<i>r

背景：在土耳其，护理教育和护理专业中缺乏人工智能应用，需要制定将人工智能融入护理专业的战略。此时，有必要转变护士可能出现的消极态度和焦虑情绪：旨在通过分析数字领导力感知的影响，即护士如何对待数字技术和创新，以及他们对如何和以何种方法使用这些技术的认识，对护理专业中的人工智能焦虑和态度的影响，来重新组织这一平行的专业转型：研究设计为描述性、相关性和横断面研究：研究采用简单随机抽样法，对土耳其三个不同地区医院的 439 名护士进行了调查：在本研究使用的数据收集工具的第一部分，使用了数字领导力量表、人工智能使用焦虑和人工智能态度量表，包括确定护士人口统计信息、他们与技术的关系、人工智能使用状况及其在职业中的重要性等问题：结果：29.8%的护士与技术关系良好，66.3%的护士了解人工智能在健康领域的应用，27.3%的护士希望人工智能能更多地融入她们的生活。据调查，护士对数字化领导力的认知处于中等水平的占 46.9%，处于较高水平的占 41.7%，82.7%的护士对人工智能持积极态度，82.7%的护士在人工智能焦虑状态调查中处于低度或中度焦虑。数字领导力与人工智能焦虑之间存在明显的负相关关系（r = -0.434；p 结论：数字领导力与人工智能焦虑之间存在明显的负相关关系：建议在数字化领导力的作用下，对人工智能的焦虑和态度可发生正向转变，并将数字化领导力现象作为将人工智能融入护理专业的实际实施策略进行评估：我们的研究表明，人工智能态度在护理数字化领导力认知对人工智能焦虑的间接影响中具有中介作用。研究发现，护士的数字化领导力认知、人工智能焦虑和人工智能态度与人口统计学变量存在显著差异。

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引用次数: 0

Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare 增强护士的能力，倡导健康平等和公平：消除偏见，在医疗保健领域实现公平、负责任的人工智能。

IF 2.4 3区医学 Q1 NURSING

Journal of Nursing Scholarship

Pub Date : 2024-07-29 DOI: 10.1111/jnu.13007

Michael P. Cary Jr PhD, RN, Sophia Bessias MPH, MSA, Jonathan McCall MS, Michael J. Pencina PhD, Siobahn D. Grady PhD, Kay Lytle DNP, RN, Nicoleta J. Economou-Zavlanos PhD

<div> <section> <h3> Background</h3> <p>The concept of <i>health equity by design</i> encompasses a multifaceted approach that integrates actions aimed at eliminating biased, unjust, and correctable differences among groups of people as a fundamental element in the design of algorithms. As algorithmic tools are increasingly integrated into clinical practice at multiple levels, nurses are uniquely positioned to address challenges posed by the historical marginalization of minority groups and its intersections with the use of “big data” in healthcare settings; however, a coherent framework is needed to ensure that nurses receive appropriate training in these domains and are equipped to act effectively.</p> </section> <section> <h3> Purpose</h3> <p>We introduce the Bias Elimination for Fair AI in Healthcare (BE FAIR) framework, a comprehensive strategic approach that incorporates principles of health equity by design, for nurses to employ when seeking to mitigate bias and prevent discriminatory practices arising from the use of clinical algorithms in healthcare. By using examples from a “real-world” AI governance framework, we aim to initiate a wider discourse on equipping nurses with the skills needed to champion the BE FAIR initiative.</p> </section> <section> <h3> Methods</h3> <p>Drawing on principles recently articulated by the Office of the National Coordinator for Health Information Technology, we conducted a critical examination of the concept of health equity by design. We also reviewed recent literature describing the risks of artificial intelligence (AI) technologies in healthcare as well as their potential for advancing health equity. Building on this context, we describe the BE FAIR framework, which has the potential to enable nurses to take a leadership role within health systems by implementing a governance structure to oversee the fairness and quality of clinical algorithms. We then examine leading frameworks for promoting health equity to inform the operationalization of BE FAIR within a local AI governance framework.</p> </section> <section> <h3> Results</h3> <p>The application of the BE FAIR framework within the context of a working governance system for clinical AI technologies demonstrates how nurses can leverage their expertise to support the development and deployment of clinical algorithms, mitigating risks such as bias and promoting ethical, high-quality care powered by big data and AI technologies.</p> </section> <section> <h3> Conclusion and Relevance</h3>

背景：通过设计实现健康公平的概念包含了一种多方面的方法，它将旨在消除群体间有偏见、不公正和可纠正的差异的行动作为算法设计的基本要素。随着算法工具越来越多地融入临床实践的多个层面，护士在应对少数群体历史上被边缘化及其与医疗保健环境中 "大数据 "使用的交叉所带来的挑战方面具有独特的优势；然而，需要一个连贯的框架来确保护士在这些领域接受适当的培训，并具备有效行动的能力。目的：我们介绍了 "在医疗保健中消除偏见以实现公平人工智能"（BE FAIR）框架，这是一种综合战略方法，其中包含了通过设计实现健康公平的原则，供护士在寻求减轻偏见和防止因在医疗保健中使用临床算法而产生歧视性做法时使用。通过使用 "真实世界 "人工智能治理框架中的实例，我们旨在发起更广泛的讨论，让护士掌握倡导 BE FAIR 倡议所需的技能：借鉴国家卫生信息技术协调员办公室（Office of the National Coordinator for Health Information Technology）最近阐述的原则，我们对 "通过设计实现健康公平 "这一概念进行了批判性研究。我们还回顾了最近的文献，这些文献描述了人工智能（AI）技术在医疗保健领域的风险及其促进健康公平的潜力。在此基础上，我们介绍了 BE FAIR 框架，该框架有可能通过实施管理结构来监督临床算法的公平性和质量，从而使护士在医疗系统中发挥领导作用。然后，我们研究了促进健康公平的主要框架，为在本地人工智能治理框架内实施 BE FAIR 提供参考：结果：在临床人工智能技术工作治理系统中应用 BE FAIR 框架，展示了护士如何利用自己的专业知识支持临床算法的开发和部署，降低偏见等风险，促进由大数据和人工智能技术驱动的合乎道德的高质量护理：随着医疗系统了解到善意的临床算法如何可能使健康差异永久化，我们有机会也有义务做得更好。赋予护士倡导 BE FAIR 的权力，让她们参与人工智能管理、数据收集方法以及旨在减少偏见的工具评估，这些新的努力标志着实现人人享有公平医疗保健的重要步骤。

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引用次数: 0

Optimizing pain management in breast cancer care: Utilizing ‘All of Us’ data and deep learning to identify patients at elevated risk for chronic pain 优化乳腺癌护理中的疼痛管理：利用 "我们所有人 "数据和深度学习识别慢性疼痛风险较高的患者。

IF 2.4 3区医学 Q1 NURSING

Journal of Nursing Scholarship

Pub Date : 2024-07-26 DOI: 10.1111/jnu.13009

Jung In Park PhD, RN, FAMIA, Steven Johnson PhD, Lisiane Pruinelli PhD, MSN, RN, FAMIA

Purpose

The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain.

Design

This study was a retrospective, observational study.

Methods

We used demographic, diagnosis, and social survey data from the NIH ‘All of Us’ program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model.

Results

The final dataset included 1131 patients. We evaluated the deep learning prediction model, which achieved an accuracy of 72.8% and an area under the receiver operating characteristic curve of 82.0%, demonstrating high performance.

Conclusion

Our research represents a significant advancement in predicting chronic pain among breast cancer patients, leveraging deep learning model. Our unique approach integrates both time-series and static data for a more comprehensive understanding of patient outcomes.

Clinical Relevance

Our study could enhance early identification and personalized management of chronic pain in breast cancer patients using a deep learning-based prediction model, reducing pain burden and improving outcomes.

目的：本研究旨在利用深度学习方法开发一种预测模型，以识别慢性疼痛高风险乳腺癌患者：本研究是一项回顾性观察研究：我们使用了来自美国国立卫生研究院 "All of Us "项目的人口统计学、诊断和社会调查数据，并使用了深度学习方法，特别是基于Transformer的时间序列分类器，来开发和评估我们的预测模型：最终数据集包括 1131 名患者。我们对深度学习预测模型进行了评估，该模型的准确率达到 72.8%，接收者工作特征曲线下面积达到 82.0%，表现出很高的性能：我们的研究代表了利用深度学习模型预测乳腺癌患者慢性疼痛的重大进展。我们的独特方法整合了时间序列和静态数据，从而更全面地了解患者的预后：我们的研究可以利用基于深度学习的预测模型，加强对乳腺癌患者慢性疼痛的早期识别和个性化管理，从而减轻疼痛负担并改善预后。

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引用次数: 0

Comparing different scoping and mapping review methodologies: A practical example using the nursing mobile workstation 比较不同的范围界定和绘图审查方法：使用护理移动工作站的实例。

IF 2.4 3区医学 Q1 NURSING

Journal of Nursing Scholarship

Pub Date : 2024-07-22 DOI: 10.1111/jnu.13005

Margot Vanmeenen MSc, RN, Julian Hirt PhD, RN, Simon Malfait PhD, RN, Ralph Möhler PhD, RN

<div> <section> <h3> Aims</h3> <p>To provide (1) an overview of core characteristics of scoping and mapping review methodologies and (2) to illustrate the differences and similarities of these methodologies using literature on nursing mobile workstations.</p> </section> <section> <h3> Design</h3> <p>Systematic review.</p> </section> <section> <h3> Methods</h3> <p>Systematic searches were conducted to identify (1) scoping and mapping review methodologies used in the field of nursing and (2) literature on nursing mobile workstations. For each systematic search, two reviewers independently screened all titles, abstracts, and full texts. We conducted narrative syntheses for both review questions. Publications on scoping and mapping review methodologies in the field of nursing were searched in MEDLINE (PubMed), Web of Science, Scopus, and CINAHL (September 2022). Publications on nursing mobile workstations were searched in MEDLINE (PubMed), CINAHL, and Web of Science (April 2022).</p> </section> <section> <h3> Results</h3> <p>We identified six scoping and mapping review methodologies (aim 1): bibliometric analysis, evidence mapping, focused mapping review and synthesis, and scoping review. The methodologies aim to provide a graphical, tabular, or narrative overview without a formal critical assessment of the literature. We provide an overview of key variables that reflect the different focus of these methodologies. We also included 26 publications on nursing mobile workstations (aim 2). Nineteen different terms were used to describe the workstations. An overall definition of the nursing mobile workstation was not found.</p> </section> <section> <h3> Conclusion</h3> <p>Scoping and mapping methodologies are regularly applied in nursing research. Although there is overlap between the different methodologies, we found some unique characteristics. Despite the regular use of nursing mobile workstations, little is known about their impact in care processes and important features. Future studies on nursing mobile workstations could explore the impact of the workstations in the care process and the current functions of the workstations. A universal definition of the workstations is warranted.</p> </section> <section> <h3> Clinical Relevance</h3> <p>Most publications address aspects of practicability of nursing mobile workstations, but we found no universal defin

目的：概述（1）范围界定和绘图综述方法的核心特征；（2）利用有关护理移动工作站的文献说明这些方法的异同：设计：系统综述：方法：进行系统检索，以确定 (1) 护理领域使用的范围界定和绘图审查方法，以及 (2) 有关护理移动工作站的文献。对于每项系统检索，均由两名审稿人独立筛选所有标题、摘要和全文。我们对这两个综述问题进行了叙述性综合。我们在 MEDLINE (PubMed)、Web of Science、Scopus 和 CINAHL（2022 年 9 月）中检索了护理领域关于范围界定和绘图综述方法的文献。在 MEDLINE（PubMed）、CINAHL 和 Web of Science（2022 年 4 月）中检索了有关护理移动工作站的文献：我们确定了六种范围界定和制图审查方法（目的 1）：文献计量分析、证据制图、重点制图审查和综合以及范围界定审查。这些方法旨在提供图表或叙述性概述，而不对文献进行正式的批判性评估。我们对反映这些方法不同侧重点的关键变量进行了概述。我们还收录了 26 篇关于护理移动工作站的文献（目的 2）。我们使用了 19 个不同的术语来描述工作站。我们没有找到护理移动工作站的整体定义：护理研究中经常使用范围界定和绘图方法。尽管不同方法之间存在重叠，但我们发现了一些独特的特征。尽管护理移动工作站经常被使用，但人们对其在护理流程中的影响和重要特点知之甚少。未来有关护理移动工作站的研究可以探讨工作站在护理流程中的影响以及工作站的现有功能。有必要对工作站进行统一定义：临床相关性：大多数出版物涉及护理移动工作站的实用性问题，但我们没有找到统一的定义。关于工作站在临床实践中的影响，目前所知甚少。

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引用次数: 0

Patient racism toward nurses in a divided society: The case of Jews and Arabs in Israel 分裂社会中病人对护士的种族主义：以色列犹太人和阿拉伯人的案例。

IF 2.4 3区医学 Q1 NURSING

Journal of Nursing Scholarship

Pub Date : 2024-07-15 DOI: 10.1111/jnu.13006

Riki Halamish-Leshem PhD, Ya'arit Bokek-Cohen PhD, Mahdi Tarabeih RN, PhD, Pazit Azuri RN, PhD

Aim

This study examines whether racism exists among Jewish and Arab patients in Israel, as reflected in patient preference for receiving treatment from a nurse with the same ethnic background.

Background

We examine the relationship between racism and the level of trust in a nurse from a different ethnic group than the patient, as well as the preferred level of social distance, in the context of ongoing conflicts between the Jewish majority and the Arab minority in Israel.

Methods

A cross-sectional study was conducted using a unique study questionnaire that asked 534 Jewish and 478 Arab respondents to express their preference for an Arab and a Jewish nurse.

Results

Among both the Jews and the Arabs, there is a similar tendency of racism toward nurses of the dissimilar ethnic group. This racism was also prevalent among participants who live in a mixed environment or those who studied or are studying and worked or work in a mixed environment. As the trust in nursing staff members from the other group increases, the level of racism decreases. The greater the social distance the participants felt from the members of the other group, the more racist the attitudes they expressed.

Conclusions

Both Jews and Arabs preferred to be treated by nurses of their own ethnic group. In contrast to the contact hypothesis theory, participants who live in a mixed environment did not express fewer racist preferences. We conclude with some useful practical suggestions aimed at decreasing racism in health care.

Clinical Relevance

Findings imply that prospective patients prefer to receive nursing care from nurses of their own ethnic group and trust these nurses more than they trust nurses of different ethnic group.

目的：本研究探讨在以色列的犹太人和阿拉伯病人中是否存在种族主义，这反映在病人是否愿意接受具有相同种族背景的护士的治疗：背景：在以色列犹太人占多数和阿拉伯人占少数的冲突持续不断的背景下，我们研究了种族主义与病人对来自不同种族的护士的信任程度以及所偏好的社会距离之间的关系：方法：采用独特的研究问卷进行了一项横断面研究，要求 534 名犹太人和 478 名阿拉伯人受访者表达他们对阿拉伯护士和犹太护士的偏好：结果：在犹太人和阿拉伯人中，对不同种族群体的护士存在类似的种族主义倾向。这种种族主义在生活在混居环境中的参与者或在混居环境中学习、工作或正在学习的参与者中也很普遍。随着对另一群体护理人员信任度的增加，种族主义的程度也会降低。参与者与其他群体成员的社会距离感越强，他们所表达的种族主义态度就越强烈：犹太人和阿拉伯人都更愿意接受本民族护士的治疗。与 "接触假说 "理论相反，生活在混居环境中的受试者所表达的种族主义倾向并没有减少。最后，我们提出了一些有用的实用建议，旨在减少医疗保健中的种族主义：研究结果表明，未来的病人更愿意接受本民族护士的护理，并且对这些护士的信任度高于对不同民族护士的信任度。

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引用次数: 0

Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient–nurse verbal communications in home healthcare settings? 合成数据扩增是否能提高机器学习分类器的性能，从而识别家庭医疗环境中病人与护士口头交流中的健康问题？

IF 2.4 3区医学 Q1 NURSING

Journal of Nursing Scholarship

Pub Date : 2024-07-03 DOI: 10.1111/jnu.13004

Jihye Kim Scroggins PhD, RN, Maxim Topaz PhD, RN, Jiyoun Song PhD, RN, Maryam Zolnoori PhD

<div> <section> <h3> Background</h3> <p>Identifying health problems in audio-recorded patient–nurse communication is important to improve outcomes in home healthcare patients who have complex conditions with increased risks of hospital utilization. Training machine learning classifiers for identifying problems requires resource-intensive human annotation.</p> </section> <section> <h3> Objective</h3> <p>To generate synthetic patient–nurse communication and to automatically annotate for common health problems encountered in home healthcare settings using GPT-4. We also examined whether augmenting real-world patient–nurse communication with synthetic data can improve the performance of machine learning to identify health problems.</p> </section> <section> <h3> Design</h3> <p>Secondary data analysis of patient–nurse verbal communication data in home healthcare settings.</p> </section> <section> <h3> Methods</h3> <p>The data were collected from one of the largest home healthcare organizations in the United States. We used 23 audio recordings of patient–nurse communications from 15 patients. The audio recordings were transcribed verbatim and manually annotated for health problems (e.g., circulation, skin, pain) indicated in the Omaha System Classification scheme. Synthetic data of patient–nurse communication were generated using the in-context learning prompting method, enhanced by chain-of-thought prompting to improve the automatic annotation performance. Machine learning classifiers were applied to three training datasets: real-world communication, synthetic communication, and real-world communication augmented by synthetic communication.</p> </section> <section> <h3> Results</h3> <p>Average <i>F</i>1 scores improved from 0.62 to 0.63 after training data were augmented with synthetic communication. The largest increase was observed using the XGBoost classifier where <i>F</i>1 scores improved from 0.61 to 0.64 (about 5% improvement). When trained solely on either real-world communication or synthetic communication, the classifiers showed comparable <i>F</i>1 scores of 0.62–0.61, respectively.</p> </section> <section> <h3> Conclusion</h3> <p>Integrating synthetic data improves machine learning classifiers' ability to identify health problems in home healthcare, with performance comparable to training on real-world data alone, highlighting the potential of synthetic data in healthcare analytics.</p> </section> <section> <h3

背景：对于病情复杂、住院风险较高的居家医疗患者来说，从患者与护士的交流录音中识别健康问题对于改善治疗效果非常重要。训练机器学习分类器来识别问题需要资源密集型的人工标注：目的：使用 GPT-4 生成合成的患者-护士交流，并自动注释家庭医疗环境中常见的健康问题。我们还研究了用合成数据增强真实世界中的护患沟通是否能提高机器学习识别健康问题的性能：设计：对家庭医疗环境中患者与护士的口头交流数据进行二次数据分析：数据收集自美国最大的家庭医疗机构之一。我们使用了来自 15 名患者的 23 份患者与护士沟通的录音。录音被逐字转录，并根据奥马哈系统分类方案中指出的健康问题（如血液循环、皮肤、疼痛）进行人工注释。使用上下文学习提示法生成病人与护士交流的合成数据，并通过思维链提示来提高自动注释性能。机器学习分类器被应用于三个训练数据集：真实世界交流、合成交流和由合成交流增强的真实世界交流：结果：在训练数据中添加合成通信后，平均 F1 分数从 0.62 提高到 0.63。使用 XGBoost 分类器观察到的增幅最大，F1 分数从 0.61 提高到 0.64（约提高 5%）。当仅在真实世界通信或合成通信中进行训练时，分类器的 F1 分数分别为 0.62-0.61 分，具有可比性：整合合成数据提高了机器学习分类器识别家庭医疗保健中健康问题的能力，其性能与仅在真实世界数据上进行的训练相当，凸显了合成数据在医疗保健分析中的潜力：这项研究表明，利用合成的患者与护士交流数据来提高机器学习分类器识别家庭医疗环境中健康问题的性能具有临床意义，这将有助于更准确、更高效地识别和检测患有复杂健康问题的家庭医疗患者。

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引用次数: 0

The quality of clinician and student quality improvement reports: An analysis of 8 years of submissions 临床医生和学生质量改进报告的质量：对 8 年来所提交报告的分析。

IF 2.4 3区医学 Q1 NURSING

Journal of Nursing Scholarship

Pub Date : 2024-06-28 DOI: 10.1111/jnu.13003

Maureen (Shawn) Kennedy MA, RN, FAAN, Jane Barnsteiner PhD, RN, FAAN

Introduction

Many papers reporting on QI projects are not publishable for a variety of reasons. We compared manuscripts submitted as QI reports between June 2014 and June 2016 (prior to publication of the revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) with papers submitted to the American Journal of Nursing between July 2016 and December 2022). The aim was to evaluate any changes in the quality of manuscripts and identify problems that led to rejection; we also compared the quality of students with non-student submissions.

Methods

We conducted a non-randomized descriptive study to evaluate 349 papers submitted as QI project reports between June 2014 and December 2022 using screening templates based on the SQUIRE 2.0 checklist and findings of the INANE Working Group on Student Papers.

Results

Manuscripts designated as QI reports accepted for publication increased from 4% during 2014–2016 (T1) to 14% during 2016–2022 (T2); one student submission was accepted. There was a slight decrease in submissions designated as QI that were not QI: 36% of student submissions during T1 and 31% of student submissions during T2. Among clinician submissions, 44% in T1 designated as QI reports were not QI versus 31% submitted during T2. There was a decrease in student submissions that followed the SQUIRE guidelines (36% during T1 to 24% during T2).

Conclusions

Findings demonstrate that by following the SQUIRE 2.0 guidelines, authors submit more complete manuscripts with fewer missing components. However, there are still misconceptions about what constitutes QI versus research and how to report QI initiatives. After comparing the findings from both periods, it is noteworthy that there is essentially the same level of inaccuracy and lack of acceptable manuscripts.

Clinical Relevance

Sharing findings from QI activities through presentations and publications is a vital way of helping spread the learnings from these projects and improve health care for a wider audience. Clinicians, academicians, and students must understand the elements of the SQUIRE guidelines and ensure that this framework is used for both designing and submitting QI projects for publication.

导言：由于各种原因，许多报告质量改进项目的论文无法发表。我们比较了 2014 年 6 月至 2016 年 6 月（《卓越质量改进报告标准》（SQUIRE 2.0）修订版出版之前）作为质量改进报告提交的稿件，以及 2016 年 7 月至 2022 年 12 月期间提交给《美国护理学杂志》的论文。目的是评估稿件质量的任何变化，找出导致退稿的问题；我们还比较了学生与非学生投稿的质量：我们进行了一项非随机描述性研究，使用基于SQUIRE 2.0检查表的筛选模板和INANE学生论文工作组的研究结果，对2014年6月至2022年12月期间作为QI项目报告提交的349篇论文进行了评估：被指定为QI报告的稿件被接受发表的比例从2014-2016年（T1）的4%增加到2016-2022年（T2）的14%；有一篇学生投稿被接受。被指定为 "QI "而非 "QI "的稿件略有减少：T1期间学生稿件占36%，T2期间学生稿件占31%。在临床医生提交的报告中，T1 阶段有 44% 被指定为 QI 报告，而 T2 阶段有 31% 不是 QI 报告。遵循 SQUIRE 指南提交的学生报告有所减少（T1 期为 36%，T2 期为 24%）：研究结果表明，通过遵循 SQUIRE 2.0 指南，作者提交的稿件更加完整，缺失部分更少。然而，对于什么是 QI 与研究以及如何报告 QI 计划，人们仍然存在误解。在比较了两个时期的调查结果后，值得注意的是，不准确和缺乏可接受稿件的情况基本相同：临床相关性：通过演讲和出版物分享质量创新活动的研究成果，是帮助传播这些项目的学习成果、为更广泛的受众改善医疗服务的重要途径。临床医生、学者和学生必须了解 SQUIRE 指南的要素，并确保在设计和提交 QI 项目供发表时使用这一框架。

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引用次数: 0

Health outcomes in children with prenatal opioid exposure with and without neonatal abstinence syndrome in the first seven years of life: An observational cohort study 产前接触阿片类药物并伴有或不伴有新生儿禁欲综合征的儿童在出生后头七年的健康状况：观察性队列研究

IF 2.4 3区医学 Q1 NURSING

Journal of Nursing Scholarship

Pub Date : 2024-06-21 DOI: 10.1111/jnu.13000

Joshua Lambert PhD, MS, Sara Arter PhD, RN, Henry Duah PhD, MPH, RN, Teenu Xavier PhD, RN, Jon E. Sprague RPH, PhD

<div> <section> <h3> Introduction</h3> <p>Prenatal opioid exposure (POE) is a major public health consequence of the opioid epidemic. Long-term health outcomes associated with POE remain unclear, especially for children with POE without a diagnosis of neonatal abstinence syndrome (NAS). Here, we aimed to describe the health outcomes of children with POE and with POE and NAS compared to unexposed children during the first 7 years of life.</p> </section> <section> <h3> Design</h3> <p>In this retrospective observational cohort study, children born between 2015 and 2022 were identified from the Maternal and Infant Data Hub (MIDH), a data repository that continuously integrates maternal, neonatal, and pediatric records from two academic medical centers and one pediatric hospital system in the Midwest, USA.</p> </section> <section> <h3> Methods</h3> <p>International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10 CM) chapters A00-N99 served as outcomes of interest. Annual incidence and crude incidence rate ratios were calculated to explore descriptive differences between the exposed and unexposed groups.</p> </section> <section> <h3> Results</h3> <p>The study included 22,002 children, 20,130 (91.5%) of whom were unexposed and 1872 (8.5%) were exposed. Of the 1872 exposed children, 371 (19.8%) received a diagnosis of NAS (POE + NAS) and 1501 were in the POE-NAS group. Across all 7 years, exposed children had a higher incidence of diagnoses in most ICD-10 CM chapters compared to unexposed children. A consistently higher incidence rate ratio of diagnosis was observed in both POE-NAS and POE + NAS groups (vs. unexposed) related to mental and behavioral disorders, eye diagnoses, and diseases of the musculoskeletal system and gastrointestinal systems.</p> </section> <section> <h3> Conclusions</h3> <p>POE is associated with an increased risk of diagnoses in a number of ICD-10 CM chapters throughout childhood. These findings underscore the need for early screening and targeted interventions to support exposed children and improve their well-being. Further research is required to explore underlying mechanisms and develop preventive measures for at-risk populations.</p> </section> <section> <h3> Clinical Relevance</h3> <p>Understanding the conditions more often diagnosed in children with prenatal opioid exposure will help to improve care p

导言：产前阿片类药物暴露（POE）是阿片类药物流行造成的主要公共卫生后果。与产前阿片类药物暴露相关的长期健康结果仍不明确，尤其是对于患有产前阿片类药物暴露但未被诊断出患有新生儿禁欲综合征（NAS）的儿童。在此，我们旨在描述患有 POE 的儿童以及患有 POE 和 NAS 的儿童与未暴露于 POE 的儿童相比，在生命最初 7 年中的健康状况。设计在这项回顾性观察队列研究中，我们从母婴数据中心（MIDH）中确定了2015年至2022年间出生的儿童，该数据中心持续整合了美国中西部地区两家学术医疗中心和一家儿科医院系统的孕产妇、新生儿和儿科记录。结果研究共纳入 22002 名儿童，其中 20130 名（91.5%）为未暴露儿童，1872 名（8.5%）为暴露儿童。在 1872 名暴露儿童中，371 人（19.8%）被诊断为 NAS（POE + NAS），1501 人属于 POE-NAS 组。在所有 7 年中，与未暴露儿童相比，暴露儿童在大多数 ICD-10 CM 章节中的诊断发生率较高。在POE-NAS组和POE + NAS组（与未暴露组相比），观察到精神和行为障碍、眼部诊断以及肌肉骨骼系统和胃肠道系统疾病的诊断发病率比率持续较高。这些发现强调了早期筛查和有针对性干预的必要性，以支持暴露儿童并改善他们的福祉。临床相关性了解产前阿片类药物暴露儿童更常被诊断出的病症将有助于改善对这一人群的护理。根据研究结果，为产前接触阿片类药物的儿童提供护理的护士可以优先进行评估，并将时间、资源和教育分配给更有可能受到影响的领域。

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引用次数: 0

Nurses' perceptions of the design, implementation, and adoption of machine learning clinical decision support: A descriptive qualitative study 护士对机器学习临床决策支持的设计、实施和采用的看法：一项描述性定性研究。

IF 2.4 3区医学 Q1 NURSING

Journal of Nursing Scholarship

Pub Date : 2024-06-19 DOI: 10.1111/jnu.13001

Ann M. Wieben PhD, RN, NI-BC, Bader G. Alreshidi PhD, RN, ACNP-BC, Brian J. Douthit PhD, NI-BC, Marisa Sileo MSN, RN, NI-BC, Pankaj Vyas MSN, MBA, RN, Linsey Steege PhD, Andrea Gilmore-Bykovskyi PhD, RN

Introduction

The purpose of this study was to explore nurses' perspectives on Machine Learning Clinical Decision Support (ML CDS) design, development, implementation, and adoption.

Design

Qualitative descriptive study.

Methods

Nurses (n = 17) participated in semi-structured interviews. Data were transcribed, coded, and analyzed using Thematic analysis methods as described by Braun and Clarke.

Results

Four major themes and 14 sub-themes highlight nurses' perspectives on autonomy in decision-making, the influence of prior experience in shaping their preferences for use of novel CDS tools, the need for clarity in why ML CDS is useful in improving practice/outcomes, and their desire to have nursing integrated in design and implementation of these tools.

Conclusion

This study provided insights into nurse perceptions regarding the utility and usability of ML CDS as well as the influence of previous experiences with technology and CDS, change management strategies needed at the time of implementation of ML CDS, the importance of nurse-perceived engagement in the development process, nurse information needs at the time of ML CDS deployment, and the perceived impact of ML CDS on nurse decision making autonomy.

Clinical Relevance

This study contributes to the body of knowledge about the use of AI and machine learning (ML) in nursing practice. Through generation of insights drawn from nurses' perspectives, these findings can inform successful design and adoption of ML Clinical Decision Support.

导言：本研究旨在探讨护士对机器学习临床决策支持（ML CDS）的设计、开发、实施和采用的看法：设计：定性描述研究：护士（n = 17）参加了半结构化访谈。采用 Braun 和 Clarke 所描述的主题分析方法对数据进行转录、编码和分析：结果：四个主要主题和 14 个次主题突出了护士对决策自主权的看法、先前经验对其使用新型 CDS 工具偏好的影响、明确 ML CDS 有助于改善实践/成果的必要性以及将护理工作纳入这些工具的设计和实施的愿望：本研究深入探讨了护士对 ML CDS 实用性和可用性的看法，以及以往使用技术和 CDS 经验的影响、实施 ML CDS 时所需的变革管理策略、护士认为参与开发过程的重要性、部署 ML CDS 时护士的信息需求，以及 ML CDS 对护士决策自主权的影响：本研究为在护理实践中使用人工智能和机器学习（ML）的知识体系做出了贡献。通过从护士的角度提出见解，这些发现可以为成功设计和采用 ML 临床决策支持提供参考。

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引用次数: 0