Introduction
With millions of infections and millions of fatalities worldwide, the COVID-19 pandemic has had an unprecedented impact on the world. There is growing concern about the pandemic’s effects on mental health in addition to the virus’s effects on physical health. Much research has been devoted to mental health in the medical industry and patients (Cullen et al., 2020; Greenberg, 2020).
A sufficiently large sample of this study allows us to assess the effectiveness of various treatment approaches and how the rates of depression differ in them. The combined effect of the virus, the general background of the news, and the environment tends to negatively affect the mental state of patients, which can be determinants of successful treatment (Kar et al., 2020). Therefore, assessing the extent to which the virus infection and treatments affect the selected groups is essential.
Reason for the Study and Hypothesis
Depression, anxiety, and trauma cases have significantly increased as a result of the COVID-19 pandemic. To help create public health policies and mental health interventions that can effectively address the needs of those affected, it is essential to understand the nature and scope of this impact. The degree and nature of mental health impact people’s experiences are influenced by demographic factors like age and gender. Therefore, knowing how these variables affect mental health can aid in creating focused interventions that cater to the particular requirements of various populations.
The null hypothesis asserts that after adjusting for demographic characteristics such as age and gender, there is no statistically significant relationship between COVID-19 therapeutic approach and psychological well-being results in U.S. patients. An alternative explanation claims that COVID-19 disease treatment methods are significantly linked to mental health outcomes in US patients and that demographic characteristics such as age and gender mitigate this association.
Statistical Test
The most revealing method for estimating the mean values in two groups is the Student’s t-test. It aims to determine if there is a significant difference between the two samples, differences in treatment methods, and mental health outcomes of depression expressed in a score on a PHQ-9 test. This method will allow the comparison of the two groups regarding treatment approaches and the acceptance or rejection of the null hypothesis.
The result will be a tool for assessing the most effective treatment methods in connection with which clinical practice can be optimized and improved. Demographic data can be used as critical, independent variables for future collective studies. Accordingly, the independent variables will be the group number by treatment method, age, and gender, while the dependent variable will be depression rates, measured by the PHQ-9 test. Mental health data were collected during treatment.
Sample and Data to Be Collected
The sample consists of men and women aged 14 to 63 years and has 200 people whose depression rates were analyzed. Each of them received one of the two treatment programs. Since the purpose of the study was to determine whether there was a difference in the effect on the mental state of patients of the chosen treatment method, the Student’s t-test was used as an assessment of the statistically significant difference between the two samples: one consists of those who underwent treatment No. 1, the second – No. 2. The significance level was taken as classical for these studies α = 0.05. Accordingly, using the parametric t-test method, it is possible to find whether the samples differ statistically significantly by treatment methods or not, which will either accept the null hypothesis or consider the alternative one to be true.
Identification of Appropriate Assumptions to Meet the Test
The basic assumptions for the correct application of the t-test must satisfy the following requirements. First, the data must be continuous – in other words, change over time. Second, random population members should be chosen for the study to extrapolate findings to a population. Given the wide range of ages and the absence of gender or race criteria, the job satisfies these conditions.
Finally, the distribution must be at least approximately normal. This fact means that most of the participants in the experiment have depression values close to the average for the sample to apply this method correctly. In addition, since two independent choices are considered, any dependence between them must be excluded, which is also confirmed by the experimental criteria.
How to Fix Violated Assumptions
If the assumptions indicated on the previous slide are not followed, the test will be declared invalid, and the values obtained in it cannot be interpreted in the usual way. The independence of the samples is dictated by the fact that in the random sampling approach for the research question posed, these data are indeed suitable since none of the selected parameters in the samples affects the neighboring group. The persistent condition suggests change over time, as evidenced by the structure and nature of the PHQ-9 test for the dependent variable. Data can be dynamic, and one of the arguments for being dynamic is time.
Patients were selected regardless of age or gender, and the sample for each group was random, allowing the results to be extrapolated to the population. A graphical method is used to check for the normality of the dependent variable. If the scatter diagram of the values plotted on the coordinate plane approximately corresponds to a straight trend line and almost does not deviate, the sample is typical. This study does not require fixing the assumptions; however, if such a condition is necessary, the best ways are to change the sample, eliminate outliers, and recalculate the primary data with pre-established conditions. The distribution diagram of depression rates is presented on the slide.
Results
As a result of this statistical test, the results presented on the slide were obtained. The number of degrees of freedom, df, is calculated using the formula for the number of participants minus one. The established significance level of 0.05 allows for the calculation of the statistical significance of the p-value, which in this work turned out to be much lower than 0.05. In addition, the t-two tail critical is less than the t-statistic modulo; therefore, the sample data differ significantly in terms of mental health expressed through depression rates. A Pearson correlation coefficient of 0.72 indicates an association between integer treatment scores of 1 or 2 and depression scores. Therefore, in the second group, where treatment approach No. 2 was used, the rates of mental disorders were higher than in the first group.
Conclusion
From the data obtained, it can be concluded that the first treatment method is more effective than the second one in terms of how patients feel psychologically.
References
Cullen, W., Gulati, G., & Kelly, B. D. (2020). Mental health in the COVID-19 pandemic. QJM: An International Journal of Medicine, 113(5), 311-312. Web.
Greenberg, N. (2020). Mental health of health-care workers in the COVID-19 era. Nature Reviews Nephrology, 16(8), 425-426. Web.
Kar, S. K., Yasir Arafat, S. M., Kabir, R., Sharma, P., & Saxena, S. K. (2020). Coping with mental health challenges during COVID-19. Coronavirus Disease 2019 (COVID-19) Epidemiology, Pathogenesis, Diagnosis, and Therapeutics, 199-213. Web.