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Using Evidence Research Paper

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Using Evidence Research Paper

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Trauma patients are susceptible to post-injury infections. We investigated the incidence, as well as risk factors for development of pneumonia in intensive care unit (ICU)-treated trauma patients (Hyllienmark, Brattstrom, Larsson, Martling, Petersson, & Oldner, 2013). The study consisted of 322 trauma patients submitted to a level one trauma center following initial resuscitation. The study looked at hospital interventions in the first 24 hours after patient was admitted and the possible association with the patient developing pneumonia within 10 days of ICU admission (Hyllienmark et al., 2013). The study “High Incidence of post-Injury pneumonia in intensive care-treated trauma patients” is quantitative in nature it looks at the numerical data collected concerning the topic of pneumonia and ICU admitted patients. This is the article that I will use to incorporate into my paper.
Define reliability and validity; explain concurrent and predictive validity.
Although reliability must be considered in relation to validity they are both defined in different ways. Reliability can be defined as the obtainment of consistent measurements over time (Schmidt & Brown, 2012). Validity of a study can be defined as the degree to which an instrument measures what it is supposed to measure (Schmidt & Brown, 2012). Validity is broken down into internal and external validity. Internal looks reviews if the independent variable has an effect on the dependent variable. In contrast, external looks to see if the results generated from the study can be generalized to other settings and times (Schmidt & Brown, 2012). The chosen study was reviewed by the regional ethical review board in Stockholm, and deemed to have validity and reliability and has received ethical approval (Hyllienmark et al., 2013). Any study to be considered valid and reliable must demonstrate that the experiments can be replicated and the information although collected over time still remained reliable.

Explain the difference between Type I and Type II errors.
When a type I error is made the researcher will make claims that a treatment or device works when in fact it does not. According to Schmidt and Brown (2012), type I errors can result in accusations of harming the patient, for promoting an intervention is effective when it’s not. This also opens researchers up to lawsuits for malpractice. Is important for researchers to collect and analyze data without bias. Although this would not eliminate error it would greatly reduce it. Some bias in research arises from experimental error and failure to take into account all possible variables (Shuttleworth, 2009). Researchers’ would rather make a type II error, this error results from a researcher who does not release a treatment or device for use despite the fact that it’s superior to what’s available for patients. According to Schmidt and Brown the type II error is causes the researcher to miss an opportunity to make money, and the patient misses an opportunity to benefit from the device or treatment discovery. The research article “High incidence of post-injury pneumonia in intensive care-treated trauma patients”gives a numerical breakdown of factors that affect the prevalence of ICU patient pneumonia occurrence. Analysis of data states that diverging results regarding influence and severity suggest that there are other possible factors may affect the patient outcome such as severity of injury that would potentiate a patient developing pneumonia (Hyllienmark et al., 2013).
What's the main difference between non-parametric and parametric measures? Non-Parametric is defined as inferential statistics involving nominal or ordinal level data to infer to the population. This form of data does not depend on parameter and as a result may be considered less reliable. Although my chosen study did not substantiate its claim valid information was given and it was deemed reliable and reviewed. Non parametric information may lack in paper trail of chain of command. Whereas parametric is defined as inferential statistical test involving internal or ratio level data to infer to the population. Numerical data for the study of ICU patients who develop pneumonia while admitted to the hospital was included in the study it was found that 85 patients or 26% developed pneumonia within a 10 day stay in ICU. It also found that ventilator acquired pneumonia occurred in 45 patients or 14% (Hyllienmark et al., 2013). .
The minimum level of significance for scientific research is a p (probability) value of .05 or less. What does this mean? Give an example
Researchers use statistics to adjust the amount of risk involved in making type I and type II errors (Schmidt & Brown, 2012). According to Schmidt and Brown risk for the error is adjusted by selecting the alpha level, which is the probability that a researcher will make a type I error. Considering a type I error is most detrimental to patient outcomes and can lead to legal actions steps are usually taken to reduce the incidence. The Minimal level of significance for scientific research is a probability value of .05 or less this means that the alpha level needs to be 0.05 or less. This confers to the chances of the researcher making a error, that would affect the outcome of the study’s results. When the alpha level is 0.05 this specifies that the probability of an error being made is 5 times out of every 100. According to Schmidt and Brown type I and type II errors have an inverse relationship. The probability of 0.05 or less means the hypothesis can be rejected, because there was a sufficient amount of data provided against the statistical hypothesis. The topic of the study regarding pneumonia and ICU admission had a probability p<0.05 (Hyllienmark et al., 2013). . This would imply that although patients can develop pneumonia while in ICU there are many outside factor that can potentiate the infection. Alone ICU admission does not imply pneumonia infection will occur.

References
HYLLIENMARK, P., BRATTSTRÖM, O., LARSSON, E., MARTLING, C.-R., PETERSSON,
J. and OLDNER, A. (2013), High incidence of post-injury pneumonia in intensive care- treated trauma patients. Acta Anaesthesiologica Scandinavica, 57: 848–854. doi: 10.1111/aas.12111
N, Schmidt, and Brown J. Schmidt &Brown. Evidence-based practice for nurses. New York: Jones and Barlett, 2012. Print.
Shuttleworth, Martyn. (Feb 5, 2009). Research Bias. Retrieved Mar 01, 2014 from
Explorable.com: http://explorable.com/research-bias…...

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