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As the context of health care is changing due to the pharmaceutical services and technological advances, nurses and other health care professionals need to be prepared to respond in knowledgeable and practical ways. Health information is very often explained in statistical terms for making it concise and understandable. Statistics plays a vitally important role in the research. Statistics help to answer important research questions and it is the answers to such questions that further our understanding of the field and provide for academic study.

It is required the researcher to have an understanding of what tools are suitable for a particular research study. It is essential for healthcare professionals to have a basic understanding of basic concepts of statistics as it enables them to read and evaluate reports and other literature and to take independent research investigations by selecting the most appropriate statistical test for their problems (Current, 2012). Statistics can be used in postpartum to help with infection control. Postpartum infections comprise a wide range of entities that can occur after vaginal and cesarean delivery or during breastfeeding.

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In addition to trauma sustained during the birth process or cesarean procedure, physiologic changes during pregnancy contribute to the development of postpartum infections. The typical pain that many women feel in the immediate postpartum period also makes it difficult to discern postpartum infection from postpartum pain (Wong, 1994-2013). One way statistics is used on the postpartum unit is by the evidence of fever. Signs of fever are usually treated as a sign of infection by the health care provider.

Monthly audits are done on the facility to evaluate how many mothers acquired a fever with signs of infection after delivery. During the monthly Obstetrics and Gynecology meetings, the report is presented and interventions are presented to reduce the rate of infections. Staff members are educated on making sure all gauze are accounted for and that everyone on labor and delivery and postpartum use proper hand washing and educate patients on vaginal care. Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures.

Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data (Trochim, 2006). One way the facility I work for uses descriptive statistic, is by having the monthly OB meetings and showing the rate of infection that postpartum mothers are acquiring after delivery. Graphs are presented with the number of infections that were acquired for the month and presented to all OB providers and the command department. With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone.

For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study (Trochim, 2006). One example that the facility uses inferential statistics is by the OB providers and the hospital command coming together to come up with a plan properly to educate staff members on ways to prevent postpartum infections.

The nominal level of measurement is the lowest of the four ways to characterize data. Nominal means “in name only” and that should help to remember what this level is all about. Nominal data deals with names, categories, or labels (Taylor, 2013). An example of nominal level measurement used at the facility is patient identification. This consists of the patient medical record number or social security number. On the patients identification band the data includes medical record number, the sponsor’s social security number, and their date of birth.

The next level is called the ordinal level of measurement. Data at this level can be ordered, but no differences between the data can be taken that are meaningful (Taylor, 2013). An example of ordinal level is patient census. The census is checked daily to see how many patients we have on the floor and what room they are in from lowest to highest. The interval level of measurement deals with data that can be ordered, and in which differences between the data does make sense. Data at this level does not have a starting point.

The Fahrenheit and Celsius scales of temperatures are both examples of data at the interval level of measurement. You can talk about 30 degrees being 60 degrees less than 90 degrees, so differences do make sense. However 0 degrees (in both scales) cold as it may be does not represent the total absence of temperature (Taylor, 2013). The fourth and highest level of measurement is the ratio level. Data at the ratio level possess all of the features of the interval level, in addition to a zero value. Due to the presence of a zero, it now makes sense to compare the ratios of measurements.

Phrases such as “four times” and “twice” are meaningful at the ratio level. Distances, in any system of measurement give us data at the ratio level. A measurement such as 0 feet does make sense, as it represents no length. (Taylor, 2013). The advantages of accurate interpretation of statistical information to improve decision- making on the postpartum unit would include giving the best quality care to all mothers and prevent postpartum infections. The department uses the data collected monthly and puts them together in a policy that helps minimize the risk of postpartum infections after delivery.

References

Current. (2012). Basic Statistical Concepts for Nurses. Retrieved from  http://www.nursingplanet.com/Nursing_Research/basic_statistical_concepts_nurses1.htm

Taylor, C. (2013). Levels of Measurement. Retrieved from  http://statistics.about.com/od/HelpandTutorials/a/Levels-Of-Measurement.htm

Trochim, W. M. K. (2006). Descriptive Statistics. Retrieved from http://www.socialresearchmethods.net/kb/statdesc.php

Trochim, W. M. K. (2006). Inferential Statistics. Retrieved from http://www.socialresearchmethods.net/kb/statinf.php

Wong, A. W. (1994-2013). Postpartum Infections. Retrieved from http://emedicine.medscape.com/article/796892-overview

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