27^th Global Nursing and Health Care Conference July 01-02, 2020 Athens, Greece

Biography:

Jungmin Lee has completed her PhD from College of Nursing, The Catholic University of Korea. She is RN, Department of Nursing, Samsung Medical Center. She has worked in the neurosurgery intensive care unit for 13 years and is very interested in research. She had a number of research and book publishing and development of practical guidelines.

Abstract:

Purpose: The goal of this study is to apply by a useful algorithm in clinical environments, verifying the effectiveness of the Evidence based guideline for fever management of critical adult patients with brain injury.

Methods: This nonequivalent control group pretest-posttest experiments involved 48 patients aged ≥ 19 years who were admitted intensive care unit after surgery due to brain injury. Authors investigated the rate of patients with reduced fever, time to decrease fever the difference of neurological status when they discharge the ICU, between either with the algorithm for fever management or not was analyzed.

Results: The rate of the patients whose fever was decreased after the first fever was the rate of the patients with controlled fever in experimental group is 4.50 times higher than the control group(95% CI: 1.22-16.60, ρ = .024). The fever of 50% of patients in experimental group reduced within less than 1 hour, while in control group, it took less than 17hours (Log rank ρ < .001), the experimental group takes 4.8 times quicker to decrease fever than control group(HR: 4.84, 95% CI: 1.79-13.11, ρ = .002). Neurological status when to admit and to discharge was1.25±2.58 points in experimental group, and 2.13±2.70 points in control group, which was no significant difference between the two groups (z=-1.43, p = .253).

Conclusion: Algorithm for fever management of brain injury patients can be a scientific evidence for nursing practice with fast decisions, improving the efficiency of health care, hence contributing to better outcomes for critically ill patients.

Biography:

Linan Cheng,female,Chinese,31years old.She is a PhD candidate of Sichuan University in China,and her major is nursing.Her research is focused on nursing education and geriatric nursing,and has published more than 30 papers in China.

Abstract:

Background: General self-efficacy is considered one of the most influential parameters affecting the quality of clinical practice and nurses’ perceived professional benefits (NPPB). Perceived organizational support (POS) is regarded as being central in understanding job-related attitudes, and it  is important to enhance POS for nurses to maintain their current employment. NPPB can further reduce nurses’ job burnout and turn-over intention. Many studies have explored the relationships among general self-efficacy, POS, nursing practice environment (NPE) and NPPB. However, a moderating effect of NPE has not been fully explored in nurses, especially among paediatric nurses.

Methods: A descriptive cross-sectional study was conducted from July to October 2018 with 300 paediatric nurses from 3 Class A tertiary hospitals in Jilin Province. The respondents completed the General Self-Efficacy Scale, Perceived Organizational Support Scale, Practice Environment Scale and Nurses’ Perceived Professional Benefits Scale. The data were analyzed using path analysis and SPSS(version 23.0, IBM).

Results: General self-efficacy and POS were significantly positively associated with NPPB, which showed that the model had a good fit to the data. NPE was found to play a partial mediating role between POS and NPPB and also had a complete mediating role between general self-efficacy and NPPB.

Conclusions: The results suggest that general self-efficacy indirectly influences NPPB, and POS directly and indirectly influences NPPB by NPE. Effective measures should be taken to improve nurses’ practice environment in hospitals to raise nurses' enthusiasm and confidence in their work.

Biography:

Abstract:

Artificial Intelligence (AI) has increasingly developed in recent years and shown huge potential in multiple areas, especial medical and nursing. As the key part of the Artificial Intelligence, Machine Learning(ML) plays an important role in analyzing health-related data and building learning models. Some algorithms have huge advances in dealing with nursing issues such as predicting readmission、predicting emergency visiting and relevant factors of some specific chronic problems, for example, Geriatric Depression、Heart Failure and etc. This paper explains how algorithms applied in different nursing scenarios and gives out simple introductions of these algorithms, including regression analysis, decision tree, reducing dimension algorithm, convolution model and so on. By analyzing how the machine learning technologies worked with nursing data in the fields of nursing management and nursing quality improvement, we hope to provide new ideas and new methods to nurses on how to deal with clinical issues in the background of Big Data and promote the development of precision nursing in the future.

Key Words: Machine Learning; Precision Nursing; Algorithm; Big data

Biography:

Mr. Fahaid Almotairi, MN, Nurse Educator at Kuwait Nursing Institute at Public Authority for Applied Education and Training. Facilitated, presented, and developed different educational activity, symposium, and program. Attended several local, GCC, and international conferences such as ICN.

Abstract:

Nurses play an important role in the disease prevention, the health promotion, and delivering primary and community care. They will be a key to the achievement of universal health coverage. Based on WHO reports there is a global shortage in the health workers especially the nurses and midwives, who represent more than 50% of the current shortage in health workers. The shortage in the nursing field has an effective role not only on quality of care but also on population health outcomes. WHO estimates that the world will need an additional 9 million nurses and midwives by the year 2030.

One of the national health strategy goals in Kuwait is the Workforce planning that aimed to maintain a high standard of patient care through a highly trained, recruit, supported, motivated, and retains that workforce. 32,000 nurses employed in Kuwait only 8.8% are Kuwaiti. Although several campaigns to create awareness about nursing profession but still below than in some of neighboring GCC countries. However, several challenges are faced in Kuwait national nursing that require for an effective strategy plan and actions. However, Kuwait has already started strategical plans to achieve the State Vision (2035) but still it needs other motivations to arrive on time. My presentation will be focused on the current challenges that been faced for Kuwaiti nursing profession, the strengths, the opportunities, and what are the possible solutions and strategic plan. What will the audience take away from your presentation? At the end of the presentations the audience will be able to : Identify what are the most common challenges in the nursing profession Discuss about the possible solutions to overcome the nursing challenges and to encourage the recruitment of Kuwaiti in the nursing profession. Emphasize the important of strategy plan to overcome the nursing challenges How will this help the audience in their job? Is this research that other faculty could use to expand their research or teaching? Does this provide a practical solution to a problem that could simplify or make a designer’s job more efficient? Will it improve the accuracy of a design, or provide new information to assist in a design problem? List all other benefits. Is this abstract connected to an organized session? If yes, please provide full session title.