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ردیف عنوان نوع
1 Predictors of nurses attitudes and knowledge towards pain management in Italy: A cross-sectional study in the hospital settings
پیش بینی کننده نگرش پرستاران و دانش نسبت به مدیریت درد در ایتالیا:یک مطالعه مقطعی در تنظیمات بیمارستان-2021
Introduction: Pain is multidimensional, and as such it is the chief reason patients require urgent health care services. If inadequately assessed and untreated, pain may negatively impact on the quality of life of the patient. Pain management is an essential part of Nursing. The aim to this study is to examine the level of knowledge and attitudes with regard to pain among Italian nurses who work in clinical settings. Methods: The Ferrell and McCaffery’s Knowledge and Attitudes Survey Regarding Pain was distributed to 266 nurses employed in one specialized hospital in Rome, Italy. The staff in the survey work in three different set- tings: the intensive care unit, the sub-intensive care unit, and an ordinary ward. Descriptive statistics were employed and a logistic regression model was performed to evaluate the factors that may influence the attitude and knowledge of care providers. Results: 49.6% of the sample correctly answered items about attitudes, 47.4% about knowledge, and 36.5% about assessment. The results show that the odds ratio of developing positive attitudes towards pain was 1.76 times higher in nurses employed in the sub-intensive care unit than in other settings. There are no statistically sig- nificant associations of knowledge between setting, sex or education. Conclusions: Our survey revealed a limited overall level of knowledge and attitudes with regards to pain man- agement among nurses. Implementing specific training for health professionals, starting with academic educa- tion, is therefore a priority. Further research is needed on a larger sample of Italian nurses. Key practice points services. What do we already know about this subject? • Pain is universal chief reason patients require urgent health care • Pain management is an essential part of Nursing. What does our study add to the already existing information • Our survey revealed and confirmed a limited overall level of knowledge and attitudes with regards to pain management among Italian nurses. • There are no statistically significant associations of knowledge • It is plausible that occur implementing specific training for nurses, between setting, sex or education. starting with academic education, and master degree.
keywords: نگرش های | دانش | مدیریت درد | پرستاری | Attitudes | Knowledge | Pain management | Nursing | KASRP
مقاله انگلیسی
2 The link between entrepreneurship and informal investment: An international comparison
پیوند بین کارآفرینی و سرمایه گذاری غیررسمی: یک مقایسه بین المللی-2020
This study explores the link between entrepreneurship and informal investment. Using Global Entrepreneurship Monitor data, we examine what types of individuals invest in new businesses. The results reveal that individuals who engage in entrepreneurial activity are, on average, three times more likely to invest in new businesses than those who do not. We also find that individuals with entrepreneurial networks are more likely to invest in new businesses. Moreover, we present estimation results for the odds ratio of business ownership/management and informal investment, as well as of entrepreneurial networks and informal investment, in each country. We find that the link between entrepreneurship and informal investment differs across countries. Specifically, while the proportion of individuals who start businesses or engage in informal investment in Japan is lower than in other countries, the relationship between entrepreneurial propensity and informal investment in Japan is the greatest among 30 Organization for Economic Co-operation and Development countries, suggesting the presence of small-world phenomena in entrepreneurship in Japan.
Keywords: Business start-up | Entrepreneurial ecosystem | Entrepreneurship | Informal investment | Odds ratio
مقاله انگلیسی
3 Prediction of sepsis patients using machine learning approach: A meta-analysis
پیش بینی بیماران مبتلا به سپسیس با استفاده از روش یادگیری ماشین: متاآنالیز-2019
Study objective: Sepsis is a common and major health crisis in hospitals globally. An innovative and fea- sible tool for predicting sepsis remains elusive. However, early and accurate prediction of sepsis could help physicians with proper treatments and minimize the diagnostic uncertainty. Machine learning mod- els could help to identify potential clinical variables and provide higher performance than existing tra- ditional low-performance models. We therefore performed a meta-analysis of observational studies to quantify the performance of a machine learning model to predict sepsis. Methods: A comprehensive literature search was conducted through the electronic database (e.g. PubMed, Scopus, Google Scholar, EMBASE, etc.) between January 1, 20 0 0, and March 1, 2018. All the studies pub- lished in English and reporting the sepsis prediction using machine learning algorithms were considered in this study. Two authors independently extracted valuable information from the included studies. In- clusion and exclusion of studies were based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results: A total of 7 out of 135 studies met all of our inclusion criteria. For machine learning models, the pooled area under receiving operating curve (SAUROC) for predicting sepsis onset 3 to 4 h before, was 0.89 (95%CI: 0.86–0.92); sensitivity 0.81 (95%CI:0.80–0.81), and specificity 0.72 (95%CI:0.72–0.72) whereas the pooled SAUROC for SIRS, MEWS, and SOFA was 0.70, 0.50, and 0.78. Additionally, diagnostic odd ratio for machine learning, SIRS, MEWS, and SOFA was 15.17 (95%CI: 9.51–24.20), 3.23 (95%CI: 1.52–6.87), 31.99 (95% CI: 1.54–666.74), and 3.75(95%CI: 2.06–6.83). Conclusion: Our study findings suggest that the machine learning approach had a better performance than the existing sepsis scoring systems in predicting sepsis.
Keywords: Area under receiver operating curve | Sepsis | Machine learning | Diagnostic odd ratio
مقاله انگلیسی
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