NUR 590 Topic 4 DQ 1 Explain whether you would select a qualitative or quantitative design to collect data and evaluate the effectiveness of your evidence-based practice project proposal
NUR 590 Topic 4 DQ 1 Explain whether you would select a qualitative or quantitative design to collect data and evaluate the effectiveness of your evidence-based practice project proposal
NUR 590 Topic 4 DQ 1
Explain whether you would select a qualitative or quantitative design to collect data and evaluate the effectiveness of your evidence-based practice project proposal. Identify which data collection tool you would specifically use and explain why this design is best for your evidence-based practice project proposal.
My PICOT: In patients with a central line (P), does use of a central line care bundle (I), when compared to no use of a central line care bundle (C), lead to lower central line associated blood infection (CLABSI) rates (O), over the course of three months (T)?
It is critical to identify the purpose of the study and collect relevant data in order to construct an evidence-based study. Statistics, despite their frightening character, “play a significant role in health and human related research… statistical analysis assists in reaching accurate conclusions, which then promotes generalization or application of findings to the full population of interest in the study” (Rebekah & Ravindran, 2021, p 62). During the three months of my PICOT intervention, I want to reduce the occurrence of CLABSI. The findings of this quantitative evaluation have encouraged me to conduct a quantitative evidence-based practice study to investigate the frequency and incidence of CLABSI and whether implementing a standard bundle will successfully reduce them. In order to have an impact on patient outcomes, it is also necessary to interpret and explain a large number of numbers, and statistical analysis is an important element of this process (Rebekah & Ravindran, 2021). Inferential statistics allow us to derive conclusions about certain treatments or circumstances by analyzing data.
To obtain PICOT data, I do not need to use intermediate or advanced statistical methods. I’d use an Excel spreadsheet instead of paper forms to generate statistics on the effectiveness of central line care bundles in lowering CLABSI rates compared to people who didn’t use them. This information would be provided by a randomized control experiment. In order to evaluate paperwork and actual nursing interventions, these patients would need access to their health data in EPIC. This straightforward comparison can be thoroughly investigated using simple Excel tools (Rebekah & Ravindran, 2021). Finding research volunteers and ensuring that they are assigned at random would be the most difficult aspects of this project, in my opinion.
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In order to reduce obesity rates, a quantitative research approach may be the most effective way to examine the impact education has on physical health changes like weight loss. It’s because gathering body measurements includes both physical number categorization and identifying nutritional levels in meals could be a significant aspect in evaluating whether certain forms of instructional content are more effective than others (Metzgar & Nickols-Richardson, 2016). A survey, in my opinion, is the greatest way to collect quantitative data for the research project because it provides enough data for evidence-based practice. It is most likely that surveys would be the most appropriate method for my research, which would involve dealing with outpatient and public facilities (Lallukka, Pietilaeinen, Jaeppinen, Laaksonen, Lahti & Rahkonen, 2020). Surveys that contain questions about changes in measures and the sort of food consumed within the time frame would provide data that can show a direct association with less cost, making them more efficient. However, instead of employing national or general public polls, I would only disseminate questionnaires to those who had been actively solicited to participate in the initiative.
References:
Lallukka, T., Pietilaeinen, O., Jaeppinen, S., Laaksonen, M., Lahti, J., & Rahkonen, O. (2020). Factors associated with health survey response among young employees: a register-based study using online, mailed and telephone interview data collection methods. BMC PUBLIC HEALTH, 20(1). https://doi-org.lopes.idm.oclc.org/10.1186/s12889-020-8241-8
Metzgar, C. J., & Nickols-Richardson, S. M. (2016). Effects of nutrition education on weight gain prevention: a randomized controlled trial. Nutrition Journal, 15, 1–13. https://doi-org.lopes.idm.oclc.org/10.1186/s12937-016-0150-4
My PICOT: In patients with a central line (P), does use of a central line care bundle (I), when compared to no use of a central line care bundle (C), lead to lower central line associated blood infection (CLABSI) rates (O), over the course of three months (T)?
Careful identification of study intentions and meaningful data collection is an essential piece in the evidence-based practice study design process. Though daunting, statistics “play a key role in health and human related research… statistical analysis assists in
arriving at right conclusions which then promotes generalization or application of findings to the whole population of interest in the study,” (Rebekah & Ravindran, 2021, p 62). My PICOT intervention aims to reduce the rate of CLABSI occurrence over the course of three months. This quantitative evaluation leads me to design a quantitative evidence-based practice study that considers the numbers and rates of CLABSI occurrence and whether or not implementing a standard bundle will effectively reduce these. Additionally, statistical analysis is essential to give meaning and a story behind a great deal of numbers, with ultimate positive impact on patient popultaion outcomes (Rebekah & Ravindran, 2021). Inferential statistics allow for statistical analysis of data collected to then draw conclusions from specific interventions or scenarios.
PICOT data collection does not necessitate the use of intermediate or advanced statistical software. Instead, I would use an Excel document to collect data on a randomized control trial approach to patient information, whether or not the intervention of a central line care bundle was implemented, and whether or not CLABSI rates were seen to be lower than those who did not use a central line care bundle. This would require access to patient health records in EPIC, as well as review of documentation and actual nurse interventions performed with this patient group. This relatively simple comparison can be analyzed using basic Excel formulas (Rebekah & Ravindran, 2021). The most difficult challenge, I believe, will be identifying those who will participate in the study and determining whether it can be done in a randomized fashion.
References
Rebekah, G. & Ravindran, V. (2021). Statistical analysis in nursing research. Indian Journal of Continuing Nursing Education, 19(1), p 62-69.
Data Collection
For my evidence-based practice proposal there are several different ways I would like to collect data all utilizing quantitative research. For the first stage of my proposal, I would like to implement teaching select members of the staff the evidence and intervention for this project. The next stage is then to have those select members teach the rest of the staff utilizing pre/post test data collection like in the research done in Ceylan et al. (2021) study. By utilizing the ARCC model these key staff members will then become the EBP monitors and will watch the rest of the study and help collect the quantitative data (Technologies, n.d.). The next stage of this evidence-based practice project will implement surveys to the patients, to quantify how many of the patients fill them out and how many are followed through by the staff. This survey will ask things like: Are you sexually active? What is your preferred partner M, F, Both? And finally, Would you like to be STD tested today? Inspiration for this step was pulled from the study by Romo et al. (2019). The next step is with the nurses- for them to ask the hard-hitting questions to find out who all the patients’ partners are, and contacting them to get tested. My hope for this last step is that there will be a place within the EMR for the nurse to document the conversation in the easiest way possible. Collecting the data for that will be mined from the EMR itself. The last bit of information will be the proof we need that this all works together to get more syphilis tests complete. Whether this be local/county data or just within this specific clinic is yet to be determined.
Blog, F. (2019, July 23). 7 Data Collection Methods & Tools For Research. Www.Formpl.Us. https://www.formpl.us/blog/data-collection-method
Ceylan, E., & Koç, A. (2021). Effect of peer education model on nursing students’ knowledge and attitudes towards HIV/AIDS. Nurse Education Today, 99, 104808. https://doi-org.lopes.idm.oclc.org/10.1016/j.nedt.2021.104808
Technologies, O. O. I. (n.d.). University of Maryland School of Nursing. Cf.Son.Umaryland.Edu. Retrieved August 29, 2021, from https://cf.son.umaryland.edu/NDNP804/module8/subtopic4.htm
Romo, D., Nagendra, G., Schechter, S., Pavlish, A., Cohall, A., & Neu, N. (2019). An educational intervention to improve provider screening for syphilis among men who have sex with men utilizing an urban urgent care center. Journal of Community Health: The Publication for Health Promotion and Disease Prevention, 44(4), 822–827. https://doi-org.lopes.idm.oclc.org/10.1007/s10900-019-0064
While the data can be complex, understanding what the data means requires advanced tools (Rebekah & Ravindran, 2018). Selection for this project would use quantitative research, with characteristics of those diagnosed without PGS would be measured. The measurement expressed in numbers and called quantitative data would be categorized or observed. PAWS (Predictive Analytics Software) is considered for use for this evidence-based practice proposal (EBP). Predictive scores assigned to each patient use both new and historical data to forecast activity, behavior, or trends. (Biscobing & Burns, 2020). Predictive models place a numerical score or value on the likelihood of a particular event happening. Correlations between different data elements produce results once data is collected, and the information is then run against selected data to generate predictions.
References
Biscobing, J., & Burns, E. (2020, September 24). What is predictive analytics? – Definition from whatis.com. SearchBusinessAnalytics. https://searchbusinessanalytics.techtarget.com/definition/predictive-analytics.
Rebekah, G., & Ravindran, V. (2018). Statistical analysis in nursing research. Indian Journal of Continuing Nursing Education, 19(1), 62–70. https://doi.org/https://www.ijcne.org/temp/IndianJContNsgEdn19162-4158908_010918.pdf
Qualitative Research Design
While quantitative research confers accurate and precise measures, the most suitable framework for the bedside shift report is the qualitative design. This is mainly because the goal is to establish the intervention effects on patient satisfaction, which is highly subjective and contextual. This design offers a deeper understanding of patients’ experience with the tool. It focuses on multiple realities of patients and may help increase knowledge about the effectiveness of the BSR framework (Dorvil, 2018). Because the study focuses on social interactions and experiences during clinical handovers, the qualitative approach may help gain new insights on how to improve the processes across the continuum of care, which is essential in attaining sustainable improvements. In this case, the most suitable instrument for data collection would be a focus group interview using semi-structured questionnaire tools. The goal is to obtain information about patient involvement.
Notably, clinical handovers often operate in a complex environment and involve patients with different needs. Because these experiences are not constant, different patients may harbor unique views about the impact of the BSR intervention. In this case, asking predefined questions helps elicit appropriate responses that show the level of patient satisfaction. Besides, semi-structured questions also help collect new insights about the intervention. According to Bressan et al. (2019), a key feature in past studies about BSR involves qualitative research. This observation corresponds with the preferred method in this study.
In summary, qualitative design is the most suitable method for this research because it hopes to interpret subjective experiences. The semi-structured interview would help elicit suitable responses that reflect the level of satisfaction. The goal is to ensure appropriate data is collected during the intervention. Doing so will help obtain common themes about patient satisfaction following the intervention.
References
Bressan, V., Cadorin, L., Pellegrinet, D., Bulfone, G., Stevanin, S., & Palese, A. (2019). Bedside shift handover implementation quantitative evidence: Findings from a scoping review. Journal of nursing management, 27(4), 815-832.
Dorvil, B. (2018). The secrets to successful nurse bedside shift report implementation and sustainability. Nursing Management, 49(6), 20.
I would select a quantitative design to collect data and evaluate the effectiveness of my evidence-based practice project. The reason I would use a quantitative design versus a qualitative design is because I am trying to quantify a problem, and need data that can be either counted or compared on a numeric scale (MAC Dewitt Wallace Library, n.d.). Whereas qualitative date would be more if I was trying to describe qualities or characteristics and use things like interviews and observation (MAC Dewitt Wallace Library, n.d.). The specific data collection tool I would use for my evidence-based practice project proposal is observation. When you think of observation, I think you usually think of qualitative data, but it can be used for quantitative data collection as well. The way we use observation to help us with the quantitative design is with a structured observation, where you identify the behavior, you intend to focus on, in my projects case that is central line associated blood infections (GCU, 2021). Then, you record the behaviors as they happen. So as patients get or do not get CLABSI’s we record that data. We can then use these observations to make improvements. We can bring in other tools like statistical analysis to analyze further why someone got the CLABSI, where we went wrong, if it was preventable or non-preventable. We can also observe adherence to the bundle through observing chart auditing. There are many tools that need to be used hand-in-hand for them to be effective, you cannot use just one. We need to observe how many CLABSI’s happen over the period of the year, how much is being charted as far as use of the CLABSI bundle and analyze where further improvements can be made.
References
GCU. (2021, December 15). The Most Effective Quantitative Data Collection Methods. https://www.gcu.edu/blog/doctoral-journey/most-effective-quantitative-data-collection-methods
MAC Dewitt Wallace Library. (n.d.). All Guides: Data Module #1: What is Research Data?: Defining Research Data. MAC Dewitt Wallace Library. https://libguides.macalester.edu/c.php?g=527786