看護とケアのジャーナル

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Objective: To illustrate the experiences and perceived benefits of healing yoga as described by patients with breast cancer participating in a healing yoga program. Methods: The qualitative research with naturalistic design was conducted after the completion of an 8 weeks healing yoga program. A total of 11 breast cancer women with adjuvant chemotherapy were interviewed using the semi-structured interview guidelines. Findings: Participants expressed their experiences of healing yoga, including transforming concern to confidence, regaining a sense of belonging, gaining experience and satisfaction, and leading the way for life, were critical in developing these benefits. The perceived benefits of participating in this program was described as positive mental support, promoted a mind-body interaction and provided benefits ascribed to social activities, leading to a reported increase in active participation in life. Conclusion: This study gives support for the positive experiences of healing yoga among patients with a breast cancer. The objective effect of the healing yoga must be examined further to guide nurses in implementing suitable health promotional strategies for breast cancer patients.

Introduction: Acute myocardial infarction (heart attack) is one of the deadliest diseases patients face. The key to cardiovascular disease management is to evaluate large scores of datasets, compare and mine for information that can be used to predict, prevent, manage and treat chronic diseases such as heart attacks. Big Data analytics, known in the corporate world for its valuable use in controlling, contrasting and managing large datasets can be applied with much success to the prediction, prevention, management and treatment of cardiovascular disease. Data mining, visualization and Hadoop are technologies or tools of big data in mining the voluminous datasets for information. Aim: The aim of this literature review was to identify usage of Big Data analytics in heart attack prediction and prevention, the use of technologies applicable to big data, privacy concerns for the patient, and challenges and future trends as well as suggestions for further use of these technologies. Methods: The national and international databases were examined to identify studies conducted about big data analytics in healthcare, heart attack prediction and prevention, technologies used in big data, and privacy concerns. A total of 31 studies that fit these criteria were assessed. Results: Per the studies analyzed, Big Data analytics is useful in predicting heart attack, and the technologies used in Big Data are extremely vital to the management and tailoring of treatment for cardiovascular disease. And as the use of Big Data in healthcare increases, more useful personalized medicine will be available to individual patients. Conclusion: This review offers the latest information on Big Data analytics in healthcare, predicting heart attack, and tailoring medical treatment to the individual. The results will guide providers, healthcare organizations, nurses, and other treatment providers in using Big Data technologies to predict and manage heart attack as well as what privacy concerns face the use of Big Data analytics in healthcare. Effective and tailored medical treatment can be developed using these technologies.

Purpose: The purpose of this study was to explore and describe the significance of clinical supervision amongst Bridging course learners at the selected hospitals in Vhembe district, Limpopo province. Methods: A qualitative, exploratory, contextual and descriptive design was used and this approach was regarded as the most appropriate for this study. The population of this study consisted of the learner nurses in the Bridging Course Programme (R683) leading to registration as a General Nurse. Purposive, non-probability sampling method was used to select the participants. Data collection: Data was collected by means of focus group discussion interviews during which participants were able to describe their experiences of the clinical placement in the real life setting and the support received from the professional nurses, data were collected until data saturation was reached. Data analysis: The researcher used the process of bracketing and remained neutral, setting aside previous knowledge and beliefs about the phenomenon under investigation. The researcher listened to the audiotapes used for data collection several times until the researcher completely satisfied with the interpretation of the verbatim data. Ethical consideration: The researcher sought for approval to collect data from the appropriate authority at the University of Venda, the Provincial Department of Health and the bridging students in each institution.

Introduction: Inappropriate initial antimicrobial therapy leads to higher mortality in patients with bloodstream infection. This study aimed to evaluate the relationship between risk factors, etiology and antimicrobial therapy on mortality rates of patients with bloodstream infection. Methods: Between January 2016 to December 2016, 167 patients with bloodstream infection were prospectively evaluated according to the presence or absence of inappropriate antimicrobial therapy of infection. Hospital mortality was the main outcome variable compared between the two study groups. Results: Infected patients who received inappropriate antimicrobial therapy had statistically more diabetes mellitus, chronic obstructive pulmonary disease, chronic renal disease and death than infected patients who initially received appropriate antimicrobial therapy. Loading dose error and error in starting antimicrobial administration were the most frequently detected error in our study and both were determinant factors related to increased mortality. Initial antimicrobial therapy was maintained, escalation and de-escalation 67.6%, 22.7% and 9.6% of cases, respectively. Coagulase negative staphylococci represented the majority reaching 40.7% and multi-drug resistant microorganisms were detected in 27.3% of infections. There was no observed difference in mortality rates among infections caused by resistant or susceptible microorganisms. Conclusion: Loading dose error and error in starting antimicrobial administration, were the most frequently detected error in our study and both were determinant factors related to increased mortality. Beside the multiple logistic regression analysis revealed that the delay in starting antimicrobial therapy was the only independent factor that increased mortality.

Aim: The aim of this study is to define and evaluate factors related to the California Critical Thinking Disposition Inventory (CCTDI) of intensive care unit nurses working at Adnan Menderes University. Methodology: The population of the study consisted of 60 nurses studying and at working at university hospitals. The sample size consisted of 40 nurses who volunteered to participate in the study. The data were collected from January to March 2012. The Socio-demographic Features Data Form and the CCTDI were used as data collection tools. This inventory was developed based on the results of the Delphi Report in which critical thinking and disposition toward critical thinking were conceptualized by a group of critical thinking experts. The original CCTDI includes 75 items loaded on seven constructs. These are inquisitiveness, open-mindedness, systematicity, analyticity, truth-seeking, critical thinking self-confidence, and maturity. The SPSS 15.0 package software was used in the evaluation of data, which employed numbers, percentage estimations, arithmetic means, the Kruskal-Wallis Test, the t test and Pearson’s correlation analysis. Results: When total score means are examined, it is seen that the mean score obtained by the nurses was 190,90 ± 20,23. The CCTDI score means of the nurses taken into the scope of the study reveal that the mean score on the “truth-seeking” subscale was 21,50 ± 5,62; the mean score on the “Open-mindedness” subscale was 36.95 ± 7.32; the mean score on the “systematicity” subscale was 19,32 ± 3,56; the mean score on the “Self-confidence” subscale was 27,75 ± 6,02; the mean score on the “Inquisitiveness” subscale was 34,47 ± 6,00. It was determined that there was no statistically significant difference between the CCDTI scale mean scores and the nurses’ ages, years of study, income levels, and education levels (p>0.05). Conclusion: This study found the nurses’ critical thinking dispositions to be at a low level. To ensure the development of a critical thinking disposition in nursing, educational opportunities must be provided inside and outside the institution.

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??: ??????????????????????????????????????????????????????????????????????????????: 2007 ? 9 ??? 2012 ? 8 ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????: 916 ?????????????????????? 544 ? (59.6 %) ????????????180??19.7%??????536??58.5%????????????82??9%????????55??6.0%??54??5.9%??????????????????716??78.2%???????????200??21.8%??????????????????????AOR 1.23?P = 0.05??HIV????????0.48?P < 0.001?????????????????AOR 2.17?P = 0.002??????????????????14???????????????????AOR 4.99?P = 0.003?????35?44??AOR 3.5?P = 0.009??25?34??AOR 2.52?P = 0.029?????? HIV ???? (AOR 4.32?P=<0.001)???????? (AOR 2.00?P=0.05)?????? 65 ??? (AOR 5.50?P=0.03) ????????????????????????????????: ??????????? 78.2% ????????????????????? (9%) ????????? HIV ???? 22.2% ?????????????????????????????????????????

??: ??????????????????????? (??????????????) ???????????????????????????????????????????????????????????: ?????????????????????????????????????????????????????????????? ???: PubMed/Medline?Spanish Medical Index?CINAHL?Scopus ? 4 ?????????????????????????: DeCS/MeSH?????????????????????????????????????????????????????????????????????????????????????????????AND/OR ???????????????????????????????????????????????????2005???2015????????????????????????????????????????????????????????????????????????????21???????????????????????85??n = 18?????????????9.5??n = 2????????????4.7??n = 1?????????????????43??n = 9???????38??n = 8??????????????9.5??n = 2????????????9.5??n = 2?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????