ObjectiveTo examine factors that are associated with the apprehension levels of oncology nurses toward hospice care. Factors examined in this study included demographics, nursing experience, education levels, title and post, personal experiences, and attitudes toward end-of-life care.MethodsQuestionnaires were provided to nurses (n=201) from three first-tier hospitals in China. A quantitative scale, Professional End-of-life Care Attitude Scale (PEAS), was used to assess personal and professional apprehension levels toward hospice care. The PEAS was translated to Chinese with terms adapted to the cultural environment in China. Statistical analyses were performed to examine the relationships between the apprehension levels and various factors.ResultsThe total PEAS scores exhibited internal consistency and reliability, with a Cronbach α=0.897 and Pearson’s r=0.9030. Of the 201 nurses, 184 provided a valid response (91.5%). Education level was significantly correlated with personal (P<0.01) and professional apprehension levels (P<0.05). Higher apprehension level was found in nurses with less education.ConclusionThe PEAS quantitative survey is useful for evaluating apprehension levels of nurses toward hospice care. Nurses with more education experienced less anxiety when providing care for terminally ill patients. The findings suggested that education programs on hospice care could be strengthened to help nurses cope with negative attitudes toward end-of-life care.
ObjectiveThe aim of this study is to investigate the attitudes of hospitalized patients with gastrointestinal cancer toward being informed of the truth and to provide references for informing patients of their gastrointestinal cancer diagnosis.MethodsNine patients with gastrointestinal cancer were selected for this study by using a purposive sampling technique from a general surgery ward in a tertiary-level general hospital in Zhejiang Province from June 2016 to October 2016. Semi-structured, in-depth interviews were conducted, and the descriptive phenomenological method (developed by Amedeo Giorgi) was used to analyze the interview data.ResultsFive themes were developed through reading, analysis, reflection, and classification of the data: Theme 1, guessing the diagnosis of gastrointestinal cancer before being informed of the truth; Theme 2, eagerness to know the diagnosis results; Theme 3, expectations related to beginning treatment for cancer; Theme 4, stress and anxiety during treatment; and Theme 5, providing patients with hope and optimism at the early diagnosis stage.ConclusionPatients have a strong desire to survive and can confidently confront their gastrointestinal cancer diagnosis. Medical staff should carefully select the appropriate time to inform patients of their diagnosis by evaluating their attitudes toward being informed, thereby actively meeting patients’ needs for information and treatment.
BackgroundThe prognosis of colorectal cancer with atypical metastasis is poor. However, atypical metastasis was less common and under-appreciated.MethodsIn this study we attempted to present the first machine learning models to predict the risk of atypical metastasis in colorectal cancer patients. We evaluated the differences between metastasis and non-metastasis groups, assessed factors associated with atypical metastasis using univariate and multivariate logistic regression analyses, and preliminarily developed the multiple machine learning models to predict atypical metastasis.Results168 patients were included. Prognostic Nutritional Index (PNI) [OR = 0.998; P = 0.030], Cancer antigen 19–9 (CA19-9) [OR = 1.011; P = 0.043] and MR-Distance [-mid OR = 0.289; P = 0.009] [-high OR = 0.248; P = 0.021] were shown to be independent risk factors for the atypical metastasis via multivariate analysis. Furthermore, the machine learning model based on AdaBoost algorithm (AUC: 0736) has better predictive performance comparing to Logistic Regression (AUC: 0.671) and KNeighbors Classifier (AUC: 0.618) by area under the curve (AUC) in the validation cohorts. The accuracy, sensitivity, and specificity of the model trained using the Adaboost method in the validation set are 0.786, 0.776 and 0.700, while 0.601, 0.933, 0.508 using Logistic Regression and 0.743, 0.390, 0.831 using KNeighbors Classifier.ConclusionMachine-learning approaches containing PNI, CA19-9 and MR-Distance show great potentials in atypical metastasis prediction.
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