NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes
Grand Canyon University NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes-Step-By-Step Guide
This guide will demonstrate how to complete the Grand Canyon University NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.
How to Research and Prepare for NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes
Whether one passes or fails an academic assignment such as the Grand Canyon University NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.
After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.
How to Write the Introduction for NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes
The introduction for the Grand Canyon University NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.
How to Write the Body for NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes
After the introduction, move into the main part of the NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.
Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.
How to Write the Conclusion for NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes
After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.
How to Format the References List for NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes
The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.
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NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes
Some current issues that have been occurring in my practice environment include HAPIs and infiltrated IVs. I work on a cardiac floor and we use IV medications such as amiodarone. One of the main side effects of amiodarone is that it can cause swelling, tenderness, pain, redness, thrombphebelitis, and/or necrosis. The worst thing that can happen is that a patient can lose a limb; unfortunately, I have seen it happen. The second most common issue I see is HAPIs or health-acquired pressure injuries. This occurs when a patient is lying on one side for too long and not turned every 2 hours (as stated in our protocol). This can be because of nurse error or a patient refused. These two concerns have led to extended hospital stays and worsening patient outcomes. Looking at regression a statistical model that can be used is linear regression. The reason I would choose linear regression is that it examines the relationship between an independent and dependent variable and I can see how strong the relationship is. Examples I would use are examining the relationship between age and the number of pressure injuries acquired to patients OR looking at the relationship between IV infiltration and the duration a patient is on IV amiodarone. For the first example, age would be the independent variable, and the number of pressure injuries would be the dependent variable. For the second example, IV infiltration would be the dependent variable and the duration a patient is on IV amiodarone would be the independent variable. For the IV infiltration example, I would measure the duration in minutes. That said, I would look at the relationship to see how many minutes it would take each participant to acquire an infiltrated IV. Looking at my pressure injury example, I would examine the relationship in a quantitative manner to see if a patient’s age has a relationship with an increased number of pressure injuries. To gather my information, I would organize the results in a spreadsheet and then put them in a linear regression graph. This type of analysis is an excellent way to find the relationship between variables. Being able to utilize this type of data can help shape hospital policies and create better outcomes for patients.
I agree with you that linear regression examines the relationship between dependent and independent variable. Both variables are important in the study. Choosing the right variables in the study may trigger confusion Therefore, an individual should identify independent and dependent variables before applying other procedures of linear regression (Diel et al., 2021). For instance, in investigating health-acquired pressure injuries as the current health issue the variables may be age and the number of the pressure injuries. Age can be identified as independent whereas HAPIs considered as dependent variable. Linear regression provides information that can be used to predict the future (Bartlett et al., 2020). The information provided in the graph reveals a trend that can be used for prediction. Healthcare settings have the information about the future helps in planning (Santiago et al., 2018). When the right data is collected for linear regression, the strategy will provide accurate and reliable information. The trends is important aspect of linear regression.
References
Bartlett, P. L., Long, P. M., Lugosi, G., & Tsigler, A. (2020). Benign overfitting in linear regression. Proceedings of the National Academy of Sciences, 117(48), 30063-30070. https://doi.org/10.1073/pnas.1907378117
Diel, A., Weigelt, S., & Macdorman, K. F. (2021). A meta-analysis of the uncanny valley’s independent and dependent variables. ACM Transactions on Human-Robot Interaction (THRI), 11(1), 1-33. https://doi.org/10.1145/3470742
Santiago, C. B., Guo, J. Y., & Sigman, M. S. (2018). Predictive and mechanistic multivariate linear regression models for reaction development. Chemical science, 9(9), 2398-2412. DOI: 10.1039/C7SC04679K
Statistical Tests That Predict Outcomes
There are a number of current issues in my practice environment as a clinical nurse. One issue is the increasing use of electronic health records (EHRs). This has led to nurses spending more time on computers and less time interacting with patients (Baumann et al., 2018). This can interfere with providing high-quality patient care. Another issue is the ever-increasing acuity of patients. This means that patients are sicker when they come into the hospital and need more complicated care. This puts a strain on nurses, who must constantly adapt to new challenges. Finally, there is a shortage of nurses across the country. This means that nurses are often overworked and understaffed. This can lead to burnout, which can have a negative consequence on the patient outcome.
A predictive statistical model is a model that can be used to make predictions about future events. In order to identify a predictive statistical model, one would need to collect data on past events and then use regression analysis to identify the best-fitting linear or nonlinear model. Once the model has been identified, researcher can use it to make predictions about future events. From the clinical issues given above, a regression model can be used to identify a predictive statistical model that can be used to predict the use of electronic health record system in the healthcare system (Mohammad & Goswami, 2021). The model can also be applied to predict the percentage increase in the acuity of patients and possible shortage or number of nurses versus the number of patients in future i.e., nurse to patient ratio. Based on the measurement of the variables (issues in healthcare practice), linear regression model can be applied to predict the future of the variables.
To develop an effective linear regression model, it is necessary to identify both the dependent and independent variable. In this case the study will involve determination of the impacts of electronic health record on shortage of nurses and the ever-increasing acuity of patients. Number of departments involved in the use of electronic health records will be the independent variable while shortage of nurses and the ever-increasing acuity rate will be dependent variable. The independent variable will be measured in terms of a continuous variable. Also, the dependent variable will be measured as continuous variables.
References
Baumann, L. A., Baker, J., & Elshaug, A. G. (2018). The impact of electronic health record systems on clinical documentation times: A systematic review. Health policy, 122(8), 827-836. https://doi.org/10.1016/j.healthpol.2018.05.014
Mohammad, P., & Goswami, A. (2021). A spatio-temporal assessment and prediction of surface urban heat island intensity using multiple linear regression techniques over Ahmedabad City, Gujarat. Journal of the Indian Society of Remote Sensing, 49(5), 1091-1108. https://link.springer.com/article/10.1007/s12524-020-01299-x
There are a number of current issues in my practice environment as a clinical nurse. One issue is the increasing use of electronic health records (EHRs). This has led to nurses spending more time on computers and less time interacting with patients (Baumann et al., 2018). This can interfere with providing high-quality patient care. Another issue is the ever-increasing acuity of patients. This means that patients are sicker when they come into the hospital and need more complicated care. This puts a strain on nurses, who must constantly adapt to new challenges. Finally, there is a shortage of nurses across the country. This means that nurses are often overworked and understaffed. This can lead to burnout, which can have a negative consequence on the patient outcome.
A predictive statistical model is a model that can be used to make predictions about future events. In order to identify a predictive statistical model, one would need to collect data on past events and then use regression analysis to identify the best-fitting linear or nonlinear model. Once the model has been identified, researcher can use it to make predictions about future events. From the clinical issues given above, a regression model can be used to identify a predictive statistical model that can be used to predict the use of electronic health record system in the healthcare system (Mohammad & Goswami, 2021). The model can also be applied to predict the percentage increase in the acuity of patients and possible shortage or number of nurses versus the number of patients in future i.e., nurse to patient ratio. Based on the measurement of the variables (issues in healthcare practice), linear regression model can be applied to predict the future of the variables.
To develop an affective linear regression model, it is necessary to identify both the dependent and independent variable. In this case the study will involve determination of the impacts of electronic health record on shortage of nurses and the ever-increasing acuity of patients. Number of departments involved in the use of electronic health records will be the independent variable while shortage of nurses and the ever-increasing acuity rate will be dependent variable. The independent variable will be measured in terms of a continuous variable. Also, the dependent variable will be measured as continuous variables.
References
Baumann, L. A., Baker, J., & Elshaug, A. G. (2018). The impact of electronic health record systems on clinical documentation times: A systematic review. Health policy, 122(8), 827-836. https://doi.org/10.1016/j.healthpol.2018.05.014
Mohammad, P., & Goswami, A. (2021). A spatio-temporal assessment and prediction of surface urban heat island intensity using multiple linear regression techniques over Ahmedabad City, Gujarat. Journal of the Indian Society of Remote Sensing, 49(5), 1091-1108. https://link.springer.com/article/10.1007/s12524-020-01299-x