Ethical global health in nursing education: An integrative review

AimThe purpose of this integrative review is to provide a comprehensive review of ethical considerations for host communities and nursing programs in planning, implementing and evaluating global health experiences for nursing students.BackgroundGlobal health experiences for nursing students are proliferating rapidly across university settings while at the same time decreasing the average time spent in the host country engaged with local communities. Global health experiences are an area where students can experience ethics as it is applied across varied contexts including resource limited international settings. As nursing education expands its global programming, exploring the ethical implications of designing, implementing and evaluating GHEs becomes pivotal to build respectful, sustainable relationships with global partners and best prepare nursing students for ethical professional practice in an interconnected world.DesignWe conducted an integrative review to examine ethical considerations in development of ethical global health experiences that benefit, rather than harm, host communities and participating nursing student guests.MethodsThe search included articles published in English language, peer-reviewed journals between 1998 and 2021 that discussed ethics in the context of nursing students traveling internationally for global health experiences. Eighteen articles met criteria for review.ResultsOverall, findings demonstrate relatively little research specific to ethical engagement in global health experiences. The articles in this integrative review discussed a range of ethical attributes including reciprocity or collaboration, respect, sustainability or commitment, justice and openness. Recommendations based on research and non-research articles are provided.ConclusionsEthical comportment in global health experiences requires careful planning, implementation and evaluation to assure an equitable and sustainable partnership between host community, faculty and nursing student guests.     

Development and validation of artificial intelligence models for preoperative prediction of inferior mesenteric artery lymph nodes metastasis in left colon and rectal cancer

BackgroundDissection of lymph nodes at the roots of the inferior mesenteric artery (IMAN) should be offered only to selected patients at a major risk of developing IMAN involvement. The aim of this study is to present the first artificial intelligence (AI) models to predict IMAN metastasis risk in the left colon and rectal cancer patients.MethodsA total of 2891 patients with descending colon including splenic flexure, sigmoid colon and rectal cancer undergoing major primary surgery and IMAN dissection were included as a study cohort, which was then split into a training set (67%) and a testing set (33%). Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression model. Seven AI algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), Decision Tree Classifier (DTC), Random Forest (RF) classifier, and Multilayer Perceptron (MLP), as well as traditional multivariate LR model were employed to construct predictive models. The optimal hyperparameters were determined with 5 fold cross-validation. The predictive performance of models and the expert surgeon was assessed and compared in the testing set independently.ResultsThe IMAN involvement incidence was 4.6%. The optimal set of features selected by LASSO included 10 characteristics: neoadjuvant treatment, age, synchronous liver metastasis, synchronous lung metastasis, signet ring adenocarcinoma, neural invasion, lymphovascular invasion, CA199, endoscopic obstruction, T stage evaluated by MRI. The most accurate model derived from MLP showed excellent prediction power with area under the receiver operating characteristic curve (AUROC) of 0.873 and produced 81.0% recognition sensitivity and 82.5% specificity in the testing set independently. In contrast, the judgment of IMAN metastasis by expert surgeon yield rather imprecise and unreliable results with a significantly lower AUROC of 0.509. Additionally, the proposed MLP had the highest net benefits and the largest reduction of unnecessary IMAN dissection without the cost of additional involved IMAN missed.ConclusionMLP model was able to maintain its prediction accuracy in the testing set better than other models and expert surgeons. Our MLP model could be used to help identify IMA nodal metastasis and to select candidates for individual IMAN dissection.     

Exploring peer tutoring from the peer tutor's perspective

IntroductionChanging working practices, student numbers, workforce demands, and deficits, have created a need to consider new ways of radiography student training. One suggestion could be to implement Peer Assisted Learning (PAL) during clinical placements. PAL utilises social constructivist theories, where peer tutors teach lower or same level tutees, reinforcing and practicing material formally taught. The aim of this study was to trial an intervention of PAL, co-designed between the university and students and evaluated to identify opportunities and challenges.MethodsUsing participatory action research 8 final year student volunteers trialled a 3-week intervention, where they delivered PAL to first years, tutoring on first year radiographic clinical practice. Focus groups were held pre and post intervention to gather qualitative data.ResultsFocus group discussions were transcribed and collectively thematically analysed. Two students and the primary researcher took part in the analysis.ConclusionStudents identified benefits and challenges to PAL. Issue around preparing for and being a peer tutor are also discussed.Further study involving experiences of first year students and clinical colleagues is required.Implications for practicePeer-tutoring has potential benefits to students to facilitate the development of skills related to image analysis and critique as well as radiographic anatomy and patient positioning.     

Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care

Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.