EBP project

2

EBP Evaluation Plan

Student’s name

Instructor

Course

Date

EBP Evaluation Plan

The collected data for any evaluation plan must be analyzed with proper statistical tools to provide meaningful results of the process in this study. The data collected in this proposed EBP was about admitted residents from a local hospital inserted with urinary catheters. The study aimed to “compare patients who have had the indwelling urinary catheter longer than 72 hours to those who had it removed before the 72 hours mark.” In this case, it is important to clean and sort data with appropriate screening tools to check for normality, outliers, and any missing values and determine the course of action in case of missing values. The best statistical tools to analyze and screen procedures for collected data are SPSS software, multivariable logistics and simple logistic regression to answer the questions mentioned in the hypothesis.

To further validate collected data and establish reliability, analysts will use a stepwise method to estimate the logistic regression because based on the predefined statistical criteria, best fitting independent variables from the model will be selected and have an overall impact on the unique characteristics of the sample analyzed (Flores-Mireles, Hreha & Hunstad, 2019). My evaluation was based on three elements: independent variable gender, CAUTI incidence, and categorical dependent variable, which fit the logistic regression test correctly in measuring the variable’s correlation. Multivariable logistic tests measured the CAUTI rate, dependent variable and independent variable (age), and data were analyzed using the SPSS software.

Based on the course readings, the assumptions of logistic regression must be met with the used variables. It implies, the dependent variable CAUTI should be answered with a yes or no in all stochastic events. In contrast, the independent variable should be categorized into age groups 0-85 years and appropriately coded with SPSS software (Pérez et al., 2017). Lastly, the outcomes of variables of interest must be explained using the logistic regression model to determine correlation and strengths.

References

Flores-Mireles, A., Hreha, T. N., & Hunstad, D. A. (2019). Pathophysiology, treatment, and prevention of catheter-associated urinary tract infection. Topics in spinal cord injury rehabilitation, 25(3), 228-240.

Pérez, E., Uyan, B., Dzubay, D. P., & Fenton, S. H. (2017). Catheter-associated urinary tract infections: challenges and opportunities for the application of systems engineering. Health Systems, 6(1), 68-76.