NUR 699 Week 6 Complete Work Nursing Essays

NUR 699 Week 6 Complete Work Nursing Essays

NUR699

NUR 699 GC Week 6 Discussion 1

When reviewing the literature and different types of evidence, there are often gaps in the findings. Are such gaps a help or a hindrance when wanting to create a change?

NUR 699 GC Week 6 Discussion 2

What is the difference between statistically significant evidence and clinically significant evidence? How would each of these findings be used to advance an evidenced-based project?

NUR 699 GC Week 6 Assignment

Evidence-Based Practice Proposal: Section G: Evaluation of Process

Details:

In 500-750 words (not including the title page and reference page), develop an evaluation plan to be included in your final evidence-based practice project. Provide the following criteria in the evaluation, making sure it is comprehensive and concise:

1. Describe the rationale for the methods used in collecting the outcome data.
2. Describe the ways in which the outcome measures evaluate the extent to which the project objectives are achieved.
3. Describe how the outcomes will be measured and evaluated based on the evidence. Address validity, reliability, and applicability.
4. Describe strategies to take if outcomes do not provide positive results.
5. Describe implications for practice and future research.

Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are required to submit this assignment to Turnitin. Please refer to the directions in the Student Success Center.

Upon receiving feedback from the instructor, refine “Section G: Evaluation” for your final submission. This will be a continuous process throughout the course for each section. NUR 699 Week 6 Complete Work Nursing Essays

ADDITIONAL INFORMATION;

What is the difference between statistically significant evidence and clinically significant evidence?

Introduction

The field of medicine is full of jargon, and one important piece of jargon is “statistically significant.” In this article we’ll define what statistical significance is and how it relates to clinical significance. We’ll also discuss some of the ways in which statistics can be misleading or make information clearer.

How is statistical significance defined?

Statistical significance is defined as the probability of obtaining a result that is not due to chance. It is a measure of how likely it is that a result is due to chance, and can be expressed by its p-value. The lower the p-value, the more likely it is that your results were not due to chance.

What does it mean to have evidence that is both statistically and clinically significant?

A result that is statistically significant is one that would be unlikely to have occurred by chance. For example, if you flip a coin 10 times and it lands on heads each time, the odds of this happening are 1 in 2. The probability of getting heads is 1 out of 2 because there are two outcomes (tails or heads) for each flip therefore, it makes sense that your outcome would be more likely than not to occur due to randomness.

A result that is clinically significant means that it’s likely to affect an intervention’s outcome. This can be measured in different ways depending on what you’re trying to do:

• Clinically significant evidence could mean something like ‘increase my income by $5k’ or ‘save lives by 20%’. Both would be good examples because they don’t have any specific numbers attached yet still show a clear directionality towards something important but those aren’t necessarily scientific ways!

What are the ways in which statistics can be misleading?

Statistics can be used to make the truth seem like a lie.

• A biased sample: If you chose your sample from just one group in the population, then you may not get an accurate picture of how things really are. For example, if I asked people who had never visited a mall before about their favorite stores and restaurants at those malls, many would say that they didn’t know anything about shopping there because they hadn’t been in any other mall before they might not have even heard of them! But if we took our information from all types of people (including those who had been to other malls), then we’d have more accurate results (and thus better evidence).

• Inconsistent conclusions: The same set of data could lead different people to reach different conclusions when looking at it objectively for example: “I’m going on vacation tomorrow” could mean something completely different depending on whether someone means traveling abroad or staying at home during their holiday season! This issue poses problems when evaluating research findings since it makes it difficult for scientists working together on projects related to health care issues because each person may interpret events differently based upon personal experiences rather than basing decisions solely upon statistical analysis which can often help clarify questions raised during discussions between team members but only if everyone agrees beforehand exactly what type(s) of evidence should be considered relevant before making any interpretations/conclusions regarding various topics ranging from dieting habits among young adults today versus earlier generations’ eating habits followed religiously by grandparents or even parents themselves

What are the ways in which statistics can make information clearer?

• Statistics can be used to explain the results of a study.

• Statistics can be used to compare the results of more than one study.

• Statistics can be used to determine if a difference between two groups is statistically significant

It is important to remember that statistical significance does not mean clinical significance.

It is important to remember that statistical significance does not mean clinical significance. Statistical significance is a measure of how likely it is that an observed effect was due to chance, while clinical significance is a measure of how important an effect is to the patient.

Clinical significance cannot be determined by simple calculations; it requires careful consideration of multiple factors such as:

• The difference between what you expect and what actually happens when you try something new or take medication (the placebo effect)

• The costs associated with treating side effects from drugs or treatments (side effects could include hospital visits)

Conclusion

To sum up, the main takeaway from this blog post is that clinicians should be mindful of the importance of using statistics correctly. We should also avoid over-reliance on them and instead use other sources of evidence to make medical decisions.