DNP 801 Topic 5 DQ 2
Grand Canyon University DNP 801 Topic 5 DQ 2-Step-By-Step Guide
This guide will demonstrate how to complete the DNP 801 Topic 5 DQ 2 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 DNP 801 Topic 5 DQ 2
Whether one passes or fails an academic assignment such as the Grand Canyon University DNP 801 Topic 5 DQ 2 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 DNP 801 Topic 5 DQ 2
The introduction for the Grand Canyon University DNP 801 Topic 5 DQ 2 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 DNP 801 Topic 5 DQ 2
After the introduction, move into the main part of the DNP 801 Topic 5 DQ 2 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 DNP 801 Topic 5 DQ 2
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 DNP 801 Topic 5 DQ 2
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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DQ: Explain the difference between statistical significance and clinical significance
REPLY TO DISCUSSION
Evidence-based practices and research in the medical field have provided enormous information that can be applied in clinical practice. However, it is important to understand the differences between statistical and clinical significance since they are important aspects in the clinical field, application of evidence-based research, as well as clinical projects. Therefore, Sharma, (2021) explains that the main difference between clinical and statistical significance is that clinical significance observes dissimilarity between two groups or two treatment modalities while statistical significance on the other hand shows whether there is any mathematical significance to the carried analysis of the results or not.
Additionally, Alsoufi, (2018) adds that statistically significant results do not necessarily mean that the results are clinically relevant because many outcomes can be statistically significant but not clinically relevant. An example of clinical significance can be explained using two different chemotherapy agents for cancer whereby drug A is less expensive as compared to the usual chemotherapy agents and it increases the survival of treated patients by five years while drug B is more expensive than the usual chemotherapy drugs and increases the survival of the treated patient five months. Drug A is considered to be clinically significant because it increases the patients’ survival by five years and it is inexpensive while the statistical significance is the difference in values recorded in analysis when differentiating the years and months of patient survival.
Statistical and clinical significance is very important and can be applied in clinical practice to improve service quality. Clinically significant research outcomes are considered to be clinically viable and can be implemented in healthcare facilities to improve the quality of healthcare services thus improving patient outcomes (Polit, 2017). From the example provided earlier in comparing the effectiveness of drugs A and B, drug A can be used in healthcare facilities in the treatment cancer patients undergoing chemotherapy thus improving their survival by five years.
References
Alsoufi, B. (2018). Statistical versus clinical significance. The Journal of Thoracic and Cardiovascular Surgery, 155(1), 344-345.DOI:https://doi.org/10.1016/j.jtcvs.2017.08.108
Polit, D. F. (2017). Clinical significance in nursing research: A discussion and descriptive analysis. International Journal of Nursing Studies, 73, 17-23. https://doi.org/10.1016/j.ijnurstu.2017.05.002
Sharma H. (2021). Statistical significance or clinical significance? A researcher’s dilemma for appropriate interpretation of research results. Saudi journal of anaesthesia, 15(4), 431–434. https://doi.org/10.4103/sja.sja_158_21
Clinical significance is the importance of the research findings. How will it affect the clinical care being provided or how will it affect
the clinical decision that needs to be made for care? While statistical significance is the knowledge that the results were based on facts and the interventions performed and not by chance or an accident that happened (Melnyk, & Fineout-Overholt, 2018). The extent to which random errors in a study will affect it, is reported by using the statistical significance which is represented by p values (Melnyk, & Fineout-Overholt, 2018). When the clinical and statistical significance of a study is not understood, it can lead to misleading reports of the study results and the effects of the study. For instance, with clinical significance, if a study does not randomly assign participants to a particular group, there is a possibility that a lot of sick people could be on one group by chance and that will affect the results of the study (Melnyk, & Fineout-Overholt, 2018).
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On the other hand, the statistical significance which is used to determine fact or chance, accepts or rejects the hypothesis, that is, what the author believes will happen. For instance, when an author compares two groups stating that the administration of pain medication to the intervention group and a placebo to the control group, stating that the medication will make a difference. The null hypothesis will be that it will not make a difference. Hence, the p value is used to determine the statistical significance. When the p value is small with large samples, it can lead to the study being reported as statistically significant but clinically insignificant while large p values with small sample sizes can be clinically significant and reported as statistically insignificant (Melnyk, & Fineout-Overholt, 2018).
When a study is clinically significant, it helps to improve the medical, physical, emotional and social aspects of care for a patient. It includes both the objective effects such as function, the duration of the illness and how long life is prolonged and the subjective effects refers to the improvement in their attitude, mood, and wellbeing, decrease in pain and increased comfort. Even though statistical significance does not mean clinical significance but it leads to the improvement in the care of the patient, therefore, in a DPI project, researchers and clinicians should not discount either but should pay attention to both to improve the outcome for the patient to decrease the chances of reporting a biased study (Sharma, 2021).
References:
Melnyk, B. M., & Fineout-Overholt, E. (2018). Evidence-based practice in nursing & healthcare: A guide to best practice. LWW.
Sharma, H. (2021). Statistical significance or clinical significance? A researcher’s dilemma for appropriate interpretation of research results. Saudi Journal of Anaesthesia, 15(4), 431.
https://doi.org/10.4103/sja.sja_158_21
Thank you for your post. I agree with you it is important as a researcher that we identify statistical and clinical significance. It is essential in the outcome of the findings so it will not be misinterpreted. Researchers must understand the clinical study and value to the clinical practice. According to Ron Wasserstein, ASA’s executive director, the P value was never meant to substitute the scientific reasoning, which is of greater interest. Clinically significant findings are those that can improve quality patient care.
Examining both the clinical significance and statistical significance can assist in improving patient care as you mentioned. Statistical analysis focuses on the P-value in order to test all hypothesis. This value can be change due to various alterations in the sample size, “the magnitude of the relationship and error” (Sharma, 2021).
An example that comes to mind that relays both clinical and statistical significance is the implementation of the ABCDEF bundle in the critical care setting. This bundle demonstrates clinical improvement of ICU patients. These improvements include decrease in mortality, use of mechanical ventilation, delirium, restrain requirements, and readmissions (Pun et al., 2020). This bundle was started in the institution I work for a few years ago however it was not fully implemented house wide. The education from the pilot unit has recently begun in order to ensure all ICU’s utilize this evidence-based tool.
Statistical significance as seen in an investigator-initiated trial is used to determine whether the results of a treatment group are different than that of a control group. This is done by selecting the appropriate statistical tests based on a sample and then calculating the corresponding P-values. Another way of looking at statistical significance is that the difference seen in the sample also exists in the population. It is simply the relationship between two variables. For example, a researcher would want to know whether changing the sound of a car horn will result in more people buying it. Clinical significance on the other hand, measures the extent that a change can create a significant impact for a patient. Clinical significance, unlike in statistical significance has no set guidelines to determine clinical importance of a response. Subjective judgment is relied upon in identifying the clinical relevance of results. It also means that the difference between effectiveness of treatments is clinically relevant (Sedgwick, 2015).
It is important to note that statistical significance is very valuable in research because it is used to depict the reliability of the study results, while clinical significance shows its relevance to clinical practice. Clinical significance should be able to tell how much change it has impacted, whether the change makes an actual difference on the lives of the subjects, sustainability of the change, its cost-effectiveness, and how easy it is to implement (Ranganathan, 2015). Both statistical and clinical significance play an essential role in a DPI project, in the sense that the validity and viability, and reliability will give credibility to the project which will positively impact the practice improvement in all aspects.
Ranganathan, P., Pramesh, C. S., & Buyse, M. (2015). Common pitfalls in statistical analysis: Clinical versus statistical significance. Perspectives in clinical research, 6(3), 169.
Sedgwick, P. (2014). Statistical Significance vs Clinical Significancehttps://sengiclinical.com/research-tips/105-statistical-clinical-significance