Week 8 Response 2/3

Not all EBP projects result in statistically significant results. Define clinical significance, and explain the difference between clinical and statistical significance. How can you use clinical significance to support positive outcomes in your project?

(ELIZ ID 5/31)

The term significant implies the topic of focus is essential and valuable. Grove, Gray & Burns (2015) highlight the important aspect, “The strongest findings of a study are those that have both statistical significance and clinical importance” (p. 355). Quantitative data is a popular form of EB research nurses utilize. A key value of quantitative data is applying analytical concepts to evaluate the level of significance associated with the topic of interest. Clinical significance centers on research data that aims to improve patient outcomes, change practice, enhance the use of resources, and often decrease morbidity and mortality rates. Comparing EB research’s clinical and statistical significance can be achieved by creating a literature data table. The collection of articles I utilized for the PICOT change project often utilized small sample sizes. Despite the small sample sizes, the data provides elderly patients at risk of fall knowledge and trends. A common theme emerged throughout the collection of articles indicating that community-based Elderly patients need enhanced care beyond the hospital setting. Patients and caregivers often lack the knowledge, energy, and resources necessary to avoid falls and high rate of readmissions or emergent care. I’m excited to implement changes to utilize community-based nurses to address the issue of falls in elderly patients and meet the targeted goals of my PICOT project.

Statistical significance is a vital aspect to consider when analyzing health care research and is used for the purpose of rejecting the “null hypothesis.” The null hypothesis occurs when the research study results show no significant statistical measure of change demonstrated by the test. An example of a related null hypothesis statement is, “There is no difference in decreased thirty-day readmission rates for elderly patients receiving community nursing after discharge versus those receiving usual care.” Conversely, I am striving to reject the null hypothesis with data showing the use of home health nurses with elderly patients at risk of falls. Transitioning home does make a difference related to outcomes. An example of significant statistical results is stated as “p = < a = 0.05,” which means the results have a 95% (or 5:100) chance of falsely rejecting the null hypothesis. Determining the statistical strength of EB research is an important step in the appraisal process. Admittedly, statistics for allied health professionals was by far one of the most challenging classes I encountered. Feels good to be further down the BSN path and be able to utilize the challenging concepts we learned in stats. The ability to appraise EB research literature is truly enhanced by evaluating the analytical aspect of the research presented and helps identify topics for future research.  

Reference:

Grove, S., Gray, J., & Burns, N. (2015). Understanding nursing research (6th ed.). St. Louis: Elsevier.

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