HLT-362V-RS5-Article Analysis Ethical Eval DQ
Article Analysis 1
|Article Citation and Permalink (APA format)||Article 1 (Wats et al., 2015). Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4442147/||Article 2 (Kalroni & Aloni, 2018). Retrieved from https://www.msard-journal.com/article/S2211-0348(17)30269-9/fulltext||Article 3 (Dastjerdi, Mirhoseini, & Mohammadi, 2015). Retrieved from http://biomedpharmajournal.org/vol8no2/investigating-the-synergistic-effects-of-transcranial-direct-current-stimulation-and-cranial-electrical-stimulation-in-treatment-of-major-depression-in-a-double-blinded-controlled-trial/|
|Broad Topic Area/Title||A quantitative analysis of the prevalence of clinical depression and anxiety in patients with prostate cancer undergoing active surveillance||Contrasting relationship between depression, quantitative gait characteristics and self-report walking difficulties in people with multiple sclerosis||Investigating the synergistic effects of transcranial direct current stimulation and cranial electrical stimulation in treatment of major depression in a double blinded controlled trial|
|Identify Independent and Dependent Variables and Type of Data for the Variables||The dependent variables in the research included the prevalence of depression and anxiety. The independent variables included age, relationship, educational status, and ethnic background. The type of data that was used was observational data that was collected using surveys and questionnaires.||The dependent variables included depression and walking in patients with multiple sclerosis. The independent variables included gender, age, and disease status. Observational data was obtained from the participants using self-rated scales and questionnaires.||The dependent variable was the severity of depression. The independent variables include age, gender, relationship status, and symptoms of depression. Experimental data was obtained by exposing the participants to treatment and control interventions.|
|Population of Interest for the Study||Men with prostate cancer||Patients with multiple sclerosis||Patients with major depression|
|Sample||313 men with prostate cancer||132 people with multiple sclerosis||30 patients with major depression|
|Sampling Method||Consecutive selection based on willingness to participate over seven month period||Purposive sampling||Random sampling|
|Descriptive Statistics (Mean, Median, Mode; Standard Deviation)
Identify examples of descriptive statistics in the article.
|Mean, standard deviation, and median were used as descriptive statistics||Mean, median, and standard deviation were used to determine clinical and demographic characteristics of the study participants.||Mean, standard deviation and median were used to determine the demographic characteristics of the participants.|
Identify examples of inferential statistics in the article.
|Logistic regression analysis||Multivariate analysis of variance and chi-square||Paired T-test, ANOVA, and chi-square|
Dastjerdi, G., Mirhoseini, H., & Mohammadi, E. (2015). Investigating the synergistic effects of transcranial direct current stimulation and cranial electrical stimulation in treatment of major depression in a double blinded controlled trial. Biomedical and Pharmacology Journal, 8(2), 1267-1274. Retrieved from http://biomedpharmajournal.org/vol8no2/investigating-the-synergistic-effects-of-transcranial-direct-current-stimulation-and-cranial-electrical-stimulation-in-treatment-of-major-depression-in-a-double-blinded-controlled-trial/
Kalron, A., & Aloni, R. (2018). Contrasting relationship between depression, quantitative gait characteristics and self-report walking difficulties in people with multiple sclerosis. Multiple sclerosis and related disorders, 19, 1-5. Retrieved from https://www.msard-journal.com/article/S2211-0348(17)30269-9/fulltext
Watts, S., Leydon, G., Eyles, C., Moore, C. M., Richardson, A., Birch, B., … & Lewith, G. (2015). A quantitative analysis of the prevalence of clinical depression and anxiety in patients with prostate cancer undergoing active surveillance. BMJ open, 5(5), e006674. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4442147/
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Topic 3 DQ 2
Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research. Provide a workplace example that illustrates your ideas.
REPLY TO DISCUSSION
To understand how hypothesis testing and confidence intervals (CI) work together we must first understand what exactly they are.
Hypothesis Tests are tests conducted by forming two opposing hypothesis (Research HA and Null Ho) and attempting to validate each in order to reach a possible outcome. Confidence Intervals are a “range of likely values of the parameter with a specified level of confidence (similar to a probability)” (Sullivan, 2022). Both of these are known as inferential methods which both rely on approximated sampling distributions. CI is used to find a range of possible values and an estimate on the overall accuracy of the parameter value. Hypothesis testing is useful because it tells us how confident we can be when drawing conclusions about the parameter of our sample population.
An example of this is testing the overall performance of a new medication being offered at a clinic. One must hypothesise the effect it will have on the patient population and try to find the parameters on the satisfaction of those taking said medication. By using these two methods in conjunction, the provider can have a good educated guess on the outcome and prepare accordingly.
Sullivan, L. (2022, January 1). Confidence Intervals. Retrieved from Boston University School of Public Health: https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_confidence_intervals/bs704_confidence_intervals_print.html
Sep 2, 2022, 11:58 PM
The hypothesis is a question the researcher would like to answer. A hypothesis drives a better outcome for patient care that goes evidence-based practice. The person must collect data in a controlled manner designated best to test the hypothesis. When using the Null hypothesis as current information, the alternative hypothesis attempts to reject the null. At the same time, the Ho and the Ha are mathematic opposites. Clinical significance is the application in improving the quality of life of an individual and provides the bridge from health research to patient care (Ambrose, 2018).
While confidence intervals and hypothesis tests are similar, they contain inferential methods relying upon sampling. The LOC is a percentage of confidence level in deciding the difficulty of rejecting the hypothesis. Most people doing this research are > 90% LOC; otherwise, the test would not be warranted. The level of significance is α=1-c. Both the LOC and level of relevance reflect how sure you are of whether the data is making the correct decision or not.
The American Heart Association guidelines for resuscitation were based on the pneumonic of ABC- Airway, Breathing, and Circulation. The pneumonic is the null hypothesis. The alternative view was the use of Circulation, airways, and breathing. The research data reflected the Ha > Ho. The concentration of effective quality chest compressions leads to a worldwide change in how CPR is performed. The LOC was high enough to recruit large city Fire Dept such as Phoenix Fire to provide data regarding cardiac arrest and outcomes.