Nursing Topic Response
Describe quantitative research designs that are used to support changes in nursing practice. Choose one and explain why you chose it. Give an example of how this research design is used to drive change in nursing practices.
Response 1: (jean)
Describe quantitative research designs that are used to support changes in nursing practice.
Quantitative research collects and analyzes numerical data. It evaluates a predetermined research question. It is a way to learn about a particular group of people (sample population) It may be carried in two ways: –
Experimental research is carried out to introduce an intervention or treatment (Brydon-Miller & Coghlan, 2020). This type of research uses statistical analysis to prove or disprove a theory. In nursing practice this may be done to find out for instance to determine if dressing A works better than dressing B on patients with leg ulcers.
In non- experimental research, no intervention or treatment takes place. Data is collected, measured, or observed to measure trends, compare situations, or validate conditions. This type of research could be carried out to support a change in nursing practice for instance an example could be using a survey to find out if nurses felt emotionally supported during the COVID-19 pandemic.
Choose one and explain why you chose it. Give an example of how this research design is used to drive change in nursing practices.
An example of a non-experimental research design that could be used to drive change in nursing practice could be to ask, “Do patients prefer telehealth appointments to speak to their physician rather than in person appointments.” The information could be collected survey and the data counted to reflect the most popular answer.
Since the COVID pandemic took us by storm, the world has changed, especially the medical field. Many patients have used tele-health appointments to see their physicians instead of taking the risk of traveling to the Dr’s office where they are at risk of catching the virus. This has also helped to protect those in the medical field as they are not coming into contact as frequently with patients who may transmit the virus.
This research would help to determine if tele-health is still a preferred method of talking to their physician or not and the outcome would dictate whether it would be used to drive a change in nursing practice. For many patients the convenience of tele-health might influence patient’s answers like not having to drive or take transportation to get the appointment, not having to ask someone to take you or go with you; no waiting in the doctor’s office and not taking up so much time in a busy day.
Response 2 (chislon)
Quantitative research designs use numbers analysis to answer a research hypothesis or question. A research design is a general plan in which the researcher incorporates several study components in an analytical and coherent way to effectively tackle the research problem. “There are six types of true experimental designs commonly reported in the scientific literature. These include: (1) two-group pretest-posttest – Subjects are randomly assigned to the experimental or control group and are measured before and after the intervention; classic or true experiment, (2) two-group posttest-only – Experimental designs when subjects are randomly assigned to an experimental or control group and measured after the intervention, (3) Solomon four-group – An experimental design with four groups—some receive the intervention, others serve as controls; some are measured before and after, others are measured only after the intervention, (4) multiple experimental groups – Experimental designs using two or more experimental groups with one control group, (5) factorial – Experimental designs allowing researchers to manipulate more than one intervention, and (6) crossover designs – Experimental designs that use two or more treatments; subjects receive treatments in a random order (Schmidt, 2017).”
I will explain some more on the Two-Group Pretest-Posttest Designs. This design is known to be the common/classic design used in experiments and this is the main reason why I chose it. Other than the Two-Group Posttest-Only Designs, this design is one of the simplest designs. A two-group pretest-posttest design is an experimental design, which compares the change that occurs within two different groups on some dependent variable (the outcome) by measuring that variable at two time periods, before and after introducing/changing an independent variable (the experimental manipulation or intervention). For example, a researcher wants to test the insulin administration education of diabetic patients. Patients in a diabetic clinic are assigned randomly between two groups, a computerized learning class or an in-person teaching and demonstration class with demonstrated knowledge check. The knowledge and technique used are measured before and after teaching for all subjects. In the end, the pretest and posttest techniques were compared to determine if learning occurred. Another is to compare the posttest knowledge and technique of both groups to see if the teaching method improved the posttest score more than another did.
What is the difference between statistical significance and clinical significance? Explain why statistically significant results in a study do not always mean that the study is clinically significant. Provide an example.
Response 1: (Erika)
The difference between statistical significance and clinical significance, from what I understood in the literature was that there may be a casual link or association in scientific studies, and expressed by a probability value. One of the examples used, that helped me to better understand it was, “a drug lowered cholesterol levels an average of 195 to 178. Analysis indicated that this decrease was statistically significant; however, because any cholesterol value below 200 is considered to be within normal range, there is no clinical significance to this finding.” In-other-words, if the tested subjects had a cholesterol above 200 and the tested drug had lowered their levels either close to the 200 or even at the 200 level, then it would have had clinical significance and would then make sense to apply it as a practice intervention. (Schmidt & Brown, 2019, p.370) Basically, statistically significant results do not always mean the results have a clinical significance because it depends on the data utilized for the study and results. As the example noted above the average was within normal range then it would not be useful in a clinical practice. Again, that’s if I understood this topic correctly and the question.
Response 2: (herman)
In research studies, there can be clinical significance or statistical significance. Clinical significance is regarded as the event where a result or a course of treatment has had genuine and quantifiable effects (Dahlberg et al., 2020). That means that the treatment under trial is relevant clinically due to the fact that it is practical in causing the effect it was intended to. In that case, it means that a research is considered to be clinically significant when its effects are practical in the real world. On the other hand, statistical significance is where an effect is likely or unlikely to happen by chance (Schober et al., 2018). That means that statistical significance depends on probability while clinical significance depends on the size effects.
According to Dahlberg et al. (2020), studies can reach statistical significance but provide evidence that is not clinically meaningful, or results could not be statistically significant but very clinically relevant. That means that the study findings or the null hypothesis being studied is found to be true but in real life, the results cannot be applied. For example, a treatment regimen could be found to be effective from the point of statistical significance, however, the treatment cannot be applied in real life due to the side effects on humans.
ORDER A PLAGIARISM-FREE PAPER HERE !!
Reply 1: (Jean)
I agree with you that quantitative research is important in conducting research data and coming up with various evidence-based practices that are required to support changes in nursing practice. I also agree with you that experimental research and non-experimental research are two of the quantitative research approaches that can be adopted to help to support changes in nursing practice (Noyes et al., 2019). However, there are other quantitative approaches that a researcher can rely on to support evidence-based practices and changes in nursing practice. These include survey research, experimental research, and correlational research. Survey research is one of the basic methods of quantitative research. Survey research is therefore used to explain the characteristics of a given population. Descriptive research is also another approach to quantitative research. Descriptive research involves explaining the statuses of various identifiable variables (Noyes et al., 2019).
Noyes, J., Booth, A., Moore, G., Flemming, K., Tunçalp, Z., & Shakibazadeh, E. (2019). Synthesizing quantitative and qualitative evidence to inform guidelines on complex interventions: clarifying the purposes, designs, and outlining some methods. BMJ Global Health, 4(Suppl 1), e000893. https://doi.org/10.1136/bmjgh-2018-000893
Reply 2 (Chislon)
I agree with you that there are different types of quantitative research methods that can be adapted to support changes in nursing practice. These include survey research, descriptive research, experimental research, correlational research, and causal-comparative research (Quick & Hall, 2015). However, one of the most widely-adopted quantitative research methods in nursing research is the cause of comparative research method. The causal-comparative research method is therefore utilized to drive changes in nursing by evaluating whether certain interventions or practice changes will have a positive effect on patient populations (Quick & Hall, 2015). For instance, the causal of comparative research method can be utilized to determine whether physical activity can help improve the outcomes of patients with cardiovascular diseases. In such a research study, the intervention would therefore be physical activities, while the expected outcomes would be an improvement in the condition of patients with cardiovascular diseases.
Quick, J., & Hall, S. (2015). Part Three: The Quantitative Approach. Journal of Perioperative Practice, 25(10), 192–196. https://doi.org/10.1177/175045891502501002
Reply 1: (Erika)
I agree with you that statistical significance differs from clinical significance in that statistical significance will simply mean that a certain event is likely to happen, while clinical significance will verify the extent to which a certain event will happen. Statistical significance is mainly focused on disapproving a certain negative and proving that a given event did not occur by chance. On the other hand, clinical significance normally seeks to prove a certain positive event and that a particular event might have occurred in a certain measured manner. Although the statistical significance of study results was far more considered in the past, in modern times, the clinical significance of studying is considered because some studies placed more importance on statistical significance, which has been found not to be so significant after all(Ranganathan et al., 2015). In medical terms, clinical significance can therefore be described as results that indicate that a given course of treatment will have quantifiable and genuine effects. On the other hand, the statistical significance will be assigned to a result where an event will be found to be more unlikely to occur (Ranganathan et al., 2015).
Ranganathan, P., Pramesh, C., & Buyse, M. (2015). Common pitfalls in statistical analysis: Clinical versus statistical significance. Perspectives in Clinical Research, 6(3), 169. https://doi.org/10.4103/2229-3485.159943
Reply 2: (Herman)
I agree with you that in research studies, both clinical and statistical significance will be considered. Clinical significance will therefore determine how many applications, for example, in pharmaceutical testing works in testing and various forms of medical research where specific implications and the magnitude of such implications can be quantified and measured (Schober et al., 2018). On the other hand, the statistical significance will have numerous broad applications, including determining the causes of an occurrence or whether an occurrence happened by chance. Statistical significance can also be useful in the early stages of pharmaceutical research to determine whether more research will be needed (Schober et al., 2018).
Schober, P., Bossers, S. M., & Schwarte, L. A. (2018). Statistical Significance Versus Clinical Importance of Observed Effect Sizes. Anesthesia & Analgesia, 126(3), 1068–1072. https://doi.org/10.1213/ane.0000000000002798