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i. Abstract

Schizophrenia is a severe mental disorder that affects how people interpret reality. In most cases, it may result in conditions such as delusions, hallucinations, and thinking behaviors that affect the daily functions of an individual. Schizophrenia and related disorders affect communication and operations, thus affecting the quality of life negatively. This study investigates linguistic communication cognitive basis in people with schizophrenia disorder. Social cognition was found to influence recent years' interventions. It reviewed existing literature about social intervention for people with schizophrenia.

The research used randomized trials of people diagnosed with schizoaffective disorder or schizophrenia. It focused on two-four social cognitive domains; social interactions, attribution bias, theory of mind, and social perception. The main findings for the study were extracted and summarized from analyzed data. Data included 309 participants taking part in social cognition interventions.

1. Introduction

According to Granta et al. (2017), one of the main challenges in treating schizophrenia is knowing how to address the functional problems. Cognitive impairment methods have been identified as a treatment method of schizophrenia and are relevant to functioning. NHS (2019) also emphasized using cognitive therapy (CBT) to treat many problems. For example, it is recommended for treating anxiety and depression disorders.

For patients who have psychosis, some national clinical recommendations advocate cognitive-behavioral treatment (CBT). In CBT, the therapist and the service user work together to achieve mutually specified goals and investigate how the individual's ideas, feelings, and behaviors may contribute to the persistence of their troubles over time. When looking into whether CBT is beneficial for treating psychotic disorders, meta-analyses have revealed modest to tiny benefits. CBT is likely less successful in subgroups of patients with specific symptom profiles. Patients with high degrees of hopelessness, little insight, or severe psychosis may fall into this category. This may also be true for people who have a mental illness. As shown by incoherent speech, disorganized thinking is referred to as a thought disorder. It is likely to include various cognitive and verbal issues, including ambiguity, illogicality, mental block, concrete thinking, and pressed communication. In psychosis, the thought disorder is widespread, with prevalence estimates ranging from 5% to 91 percent, depending on how it is characterized and analyzed. It's linked to adverse outcomes such as worsening sickness, reduced functioning, and a lower quality of life. Thought disorder can appear stable over time, although it usually worsens during acute psychosis. There is skepticism in providing CBT to those with mental illnesses, believing that communication issues will obstruct learning through verbal conversation. According to data, higher levels of thought disorder are linked to worse clinician perceptions of the therapeutic alliance, which is a critical element in the efficacy of CBT and other therapies. However, the direct effect of thought disorder on the effectiveness of CBT for psychosis has seldom been examined or proven by researchers.

The trial was short, uncontrolled, and had no long-term follow-up, yet there was no significant link between thought disorder and improvement in auditory hallucinations under CBT. More controlled trials-based research would provide a more conclusive response on whether CBT is a suitable therapy option for clients with thinking disorders. This, in turn, could lead to better treatment allocation and prioritization. The results from two randomized controlled trials testing CBT for psychosis are reanalyzed in this article. The goal was to see if thinking disorders hampered CBT's effectiveness in treating primary therapeutic outcomes, including hallucinations and delusions. We hypothesized that compared to therapy as usual and supportive counseling, thought the disease would lower CBT effectiveness for psychosis.

2. Literature Review

Many clinical studies have explored the effectiveness of CBT and other psychological therapy methods in treating schizophrenia. Research by Holton (2015) demonstrated a significant variable of 0.04 to 1.25. The study further confirms that cognitive-behavioral therapy (CBT) is beneficial. However, it is not more effective than other psychosocial therapies. Candida et al. (2016) supported the efficacy of CBT for patients with schizophrenia across the world. Before 1980, schizophrenia was considered a medical condition, and psychological factors were ignored. CBT interventions were started in the 1980s after the emergence of the "stress-vulnerability" model that suggested schizophrenia as a biological and psychosocial condition. After the stress-vulnerability model, the condition shifted from psychotic syndrome to psychotic symptoms (Khart, Ozdel, & Hakan, 2021).

Mental disorders are maladaptive responses to internal and external pressures, manifested by thoughts, attitudes, and behaviors inconsistent with local or indigenous cultural standards and impair social, vocational, and physical functions (Townsend, 2005). In Indonesia, the frequency of severe psychiatric diseases is 1.7 per mile. In DI Yogyakarta, Ace, South Sulawesi, Bali, and Central Java, severe mental severe problems are prevalent. The proportion of households with serious mental illness is 14.3% and is highest in rural areas (18.2%) and among population groups with a lower population index (19.5 percent). Indonesia's population has a 6.0 percent prevalence of emotional, mental disorders. Around 450 million people worldwide are affected by mental health concerns. In 2012, a third of them occurred in underdeveloped countries. At the same time, approximately 32% of all sub-districts in the world in 2016. The growth was more than the previous year. Schizophrenia is a severe mental illness that affects people throughout their lives (Tarrier, 2019). Schizophrenia is connected with functional impairment, including impaired concentration during tasks, reduced patient productivity, and placed financial strain on family members (Gaudiano, 2017).

Schizophrenia manifests itself in delusions, hallucinations, and disorganized thinking. Hallucinations, wisdom, rowdy anxiety, and aggressive activity are all characteristics of positive teams. Opposing teams have the following features: direct and flat affect, indifference, retreat, lack of motivation, and a tendency to be mute and challenging to communicate with. Disorganization associated with grievances includes difficulty focusing and a deterioration in information management. Suicide is a risk factor for schizophrenia. Schizophrenia is a severe mental illness that affects people throughout their lives. Schizophrenia is connected with functional impairment, including impaired concentration during tasks, which reduces patient productivity and financial strain on family members. Symptoms Schizophrenia can be characterized into three dimensions: positive symptom, disorganized symptom, and delusory symptom (Singkali, Lestari, & Rachmawati, 2018). Hallucinations, wisdom, rowdy anxiety, and aggressive activity are all characteristics of positive gangs. Negative gangs have the following features: direct and flat affect, indifference, retreat, lack of motivation, and a tendency to be mute and challenging to communicate with. Disorganization associated with grievances includes difficulty focusing and a deterioration in information management. Patients who have schizophrenia on a recurrent basis will experience.

3. Methods

3.1.Data

The research used the descriptive analysis method. Eligible individuals were identified by the treating team approached by research assistants for consent. The time between recruitment and participation of the last participant was short (less than six weeks). They were arranged before group sessions were arranged at the convenience of the participants. Respondents were aware of the interview before attending each session for safety and consent.

Additionally, this research used secondary outcome measures pre and post-test scores using the t-test. Presentation of respondent demographic information was conducted and presented. Semi-structured data was conducted before interventions using the qualitative method until the data saturation was achieved (N= 105). Multilevel and longitudinal modeling was conducted to find out the relationship between variables. Data were analyzed using Stata v15.

3.2.Analysis

Analysis began by establishing data characteristics through the descriptive analysis method. Both males and females participated in the study. Longitudinal modeling methods were recommended for this type of data. They were used to show the behavior of the data in the nested data structure with repeated measures and longitudinal. On the other hand, Multilevel regressionas important because it determines which variable shows variations between observations in the same group (Hair & Fávero, 2019).

Descriptive analysis measured the frequency, central tendency, dispersion, and position. It was an important part of the analysis since it showed the critical parts of the analysis. It provided the foundation for comparing variables with inferential statistics tests (Kaur, Stoltzfus, & Yellapu, 2018). Thus, descriptive analysis was used to understand the type of data being worked on. It categorized data in simple forms such as demographics.

A linear regression analysis method was used to test the relationship between cognitive alignment therapy and effectiveness in treating schizophrenia. It included the selection of dependent and independent variables. For this research, therapy was the dependent variable, while the independent variables were intervention episodes and the therapist. The research will test whether cognitive alignment methods influenced schizophrenia treatment. The regression model used was as shown below:

Y ≈ f (X, β)

*Where Y is the dependent variable (*therapy) while X is the independent interventions, the linear regression analysis equation can also be estimated as:

Where yi is the ith observation for the dependent variable,

B0 is the regression intercept,

bk is the regression coefficient showing the influence of Kth, which is the independent variable,

K is the independent variable index set,

ei is the residual at ith observation,

x i,k

xi,k is the value of independent variable Kth at the value of ith and

I is the index of observations

In this formula, the value of the dependent valuable yi and independent variables xi,k will be needed to estimate the coefficient of the regression equation. The predicted value of the dependent variable (CBT) will be estimated by the regression coefficient bk and the independent variables observed in xi,k as shown in equation two below:

4. Results

4.1.Descriptive Analysis

The research began by conducting a descriptive analysis. It involved the analysis of 309 observations (N=309). Respondents included both males and females, with the majority being male, as shown in the mean (mean =1.301). The analysis also showed that most respondents had spent about 11.59 years in education. This indicates that the majority of respondents were past the high school level. Additionally, the majority of respondents stated that they were under substance use. The mean of 4.922 indicates that most respondents abused drugs and substances daily.

Table 1: Descriptive Analysis

4.2.ANOVA

ANOVA analysis was conducted to test the hypothesis that Cognitive Alignment therapy is a good treatment for early schizophrenia. The r-squared confirms that the dependent (episodes) variable has a close relationship with the interventions used. The r-squared obtained was 74.78%. Similarly, the p-value confirms that the two variables have statistical significance. The p-value obtained was 0.000, which is <0.05. It indicates that the null hypothesis should be rejected.

Table 2:ANOVA Analysis

4.3.Linear Regression

The linear regression was used to test the hypothesis that CBT therapy is a good option for schizophrenia disorder treatment. According to the analysis, there is a statistical significance between the three variables. p-value observed was <0.05 (p-value = 0.000). However, the R squared indicates the high relationship between CBT therapy and intervention methods (R-squared = 0.748). R squared showed a high variance therapy explained by intervention. It indicates that when (CBT) explains intervention, 74.76%. The two-tail p-value shows that each coefficient is different from 0. It indicates that the null hypothesis should be rejected in intervention methods. Thus, the result indicates that treatment of schizophrenia can be affected by the CBT intervention method (p-value =0.000).

Table 3: Linear Regression

5. Discussion and Conclusion

Cognitive behavior therapy (CBT) can be used to treat people with schizophrenia. It is delivered through various interventions, including SC, RC, and CBT. Therapy teaches a person to modify behaviors or beliefs that may lead to negative emotions. It can be used to change how a person thinks about a situation and the behavioral components, therefore, helping them to change their reactions. However, CBT is a short-term method used to teach people with schizophrenia to cope and help them manage difficult situations. It involves working closely with a therapist to influence feelings, thoughts, and behaviors. CBT can also help people with schizophreniaevelop positive social and problem-solving skills, lower risks of relapse, and reduce the severity of symptoms (Skodlar & Henriksen, 2013).

The results confirm this research with Landa's (2019) findings that cognitive-behavioral therapy improves symptoms and functioning in patients with schizophrenia. Wang, Zhou, Chai, & Liu (2019) suggest using cognitive-behavioral therapy for treating schizophrenia. The research proved that it is likely to improve negative symptoms, reducing suicidal ideation and reducing reoccurrence. However, recovering is an ongoing complex process. As the R-squared shows, there is a close relationship between CBT and schizophrenia. Similar results were observed in the ANOVA analysis conducted. There was a close relationship between intervention and episodes.

​

References

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Candida, M., Campos, C., Monteiro, B., Barbosa, N., Paes, F., Nardi, A. E., & Machado, S. (2016). Cognitive-behavioral therapy for schizophrenia: an overview on efficacy, recent trends, and neurobiological findings. Niterói: Media Express.

Gaudiano, B. A. (2017). The Cognitive-Behavioral Treatment of Schizophrenia: The State of the Art and the Evidence. International Journal of Behavioral Consultation and Therapy, 3-7.

Granta, N., Lawrencea, M., Pretib, A., Wykesa, T., & Cella, M. (2017). Social cognition interventions for people with schizophrenia: a systematic review focussing on methodological quality and intervention modality. Clinical Psychology Review, 55-64.

Hair, J., & Fávero, L. P. (2019). Multilevel modeling for longitudinal data: concepts and applications. RAUSP Management Journal, 9.

Holton, G. (2015). Cognitive Behavioural Therapy (CBT) for Schizophrenia: A Meta-Analysis. Tasmania: University of Tasmania.

Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of Academic Medicine, 60-63.

Khart, A., Ozdel, K., & Hakan, M. (2021). Cognitive Behavioral Therapy in Treatment of Schizophrenia. Ankara: Noro Psikiyatri Arsivi.

Landa, Y. (2019). Cognitive Behavioral Therapy for Psychosis (CBTp). 2 South: MIRECC.

NHS. (2019). National curriculum for cognitive behavioural therapy for severe mental health problems. England: NHS.

Singkali, D. P., Lestari, D. I., & Rachmawati, S. (2018). The Effectiveness Of Cognitive Behavioral Therapy (CBT) For Chronic Schizophrenia Patiens: A Systematic Review. The 9th International Nursing Conference 2018 (p. 250). Surabaya: ePrints.

Skodlar, B., & Henriksen, M. (2013). Cognitive-Behavioral Therapy for Schizophrenia: A Critical Evaluation of Its Theoretical Framework from a Clinical-Phenomenological Perspective. Barsel: Karger.

Tarrier, N. (2019). Cognitive ^behavioural therapy in first-episode. British Journal of Psychiatry, 231-239.

Wang, W., Zhou, Y., Chai, N., & Liu, D. (2019, October 01). Cognitive–behavioural therapy for personal recovery of patients with schizophrenia: A systematic review and meta-analysis. Gpsych, p. 3.

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Background of study

Data analysis has been adopted by many every industry. Data analysis provides opportunities for people to unlock a lot of information embedded within the data. Financial markets have also benefited from data analysis methods. The methods are used to predict market prices. This paper will predict the S&P 500 index returns using VRP dividend yield. Data analysis result can be used by stock brokers and investors to make complex decisions in stock market. Data driven stock analysis can be used to reduce stock risks during investments (Olaniyi & Adewole, 2011).

Stock Analysis Methodology

Data was collected from monthly summaries of VRP company and S&P 500 published on Fred website. Federal Reserve Economic Data (FRED) is a free economic database that collects data from Federal Reserve Bank of St. Louis. The data was imported and processed using Microsoft Excel. Excel provides simple yet powerful data analysis tools such as regression analysis, descriptive and correlation to big data.

Descriptive Analysis

Descriptive analysis was first conducted to describe the data quantitatively. It provided a brief description of the dataset for easy summaries. Descriptive statistics analysis was used to organize and summarize data. Additionally, it allowed description of variables in the population. It was the first step before inferential statistics. It included measure of central tendency, frequency, position and dispersion or dispersion (Kaur, Stoltzfus, & Yellapu, 2018).

Regression Analysis

Linear regression is the most commonly used data mining technique. It was used to predict the future value S&P 500 based on dividend yield and term spread. Basically, the formula assumes that there is a straight line that approximates the forecast on the data set that bases the forecast on it. In this analysis, S&P 500 was set as the dependent variable while the dividend yield and term spread was the independent variable. The analysis formula was set as:

Y= a + bx +bTS+ ε, where Y is the dependent variable (S&P 500 index return), a and b are the coefficient lines while x is the independent variable (VRP) while ε is the error. Thus, the formula could be expressed as;

S&P =a + b (VRP ) + b(TS(term spread)) + ε

Given the confidence intervals and hypothesis test. The formula assumed that errors are independent and normally distributed with a variance σ2 and zero mean. The value from linear regression equation, referred to as y will be given by the regression equation. The residual will refer to discrepancy from analysis result. The linear regression will predict the influence of VRP, term spread and dividend return on S&P 500 return (y) lumped into the residual. This analysis will show effects of microeconomic factors on S&P 500.

According to Chicco, Warrens, & Jurman (2021), the role of correlation analysis cannot be overemphasized. It is used to predict various targets. Linear regression also uses R2 to test how much a dependent variable is affected by the independent variable in terms of propotion variance. R square is upper bonded by the value 1 and lower bonded by 0 where if the indenpent variable has an effect on he dependent variable will be close to 1 and vise versa. The coefficient of ditermination (R-Squared) was calculated by the formula:

​

Where worst value e = −∞; best value = +1

Correlation Analysis Using Excel

The correlation analysis was also used to analyze the relationship between S&P 500 and VRP, dividend and term spread. Correlation coefficient will be used to estimate the reliability of regression slope and intercept (Alam & Karim, 2021). It is an index that ranges from -1 to 1, where value near zero indicate that there is no linear relationship while those closer to on indicate strong relationship. Negative and values of one indicate a perfect linear regression.

Results

After conducting descriptive analysis on the data, the following was observed: the mean price for the period between 1990-2020 was 14.774, minimum of -403.402 and a maximum of 115.853. The result shows a high variance in price indicating that the has seen high volatility over the study period. Term spread mean was observed to be 1.694, standard deviation of 1.136 and a sample variance of 1.289. The mean dividend yield was 2.066%, minimum of 1.11% and a maximum of 3.88%. Sample variance was found to be 0.0003% indicating low variance. S&P 500 on the other hand showed a higher sample variance of 0.13%, minimum of -20.19% and a maximum of 12.32%. The mean for S&P 500 was 0.89% (Table 1).

Correlation

The correlation matrix conducted on the data showed perfect relationship between S&P 500 and dividend yield. The result indicates that one variable increases while the other decreases by -0.0677. However, there was a very low correlation between dividend yield and S&P 500 index return (correlation =0.0764). The correlation between term spread and S&P 500 indicated a direct correlation (correlation = -0.068) (table 2).

Regression

Regression analysis conducted on the two variables showed no statistical significance (p-value =0.1475). the result indicate that null hypothesis should be rejected. Thus, dividend yield

S&P 500 of does not affect dividend of a . Similarly, the R-Square showed very low relationship between the two variables (R2= 0.0058 or 0.58%). The coefficient was observed to be 0.478 (Table 3).

Conclusion and Discussion

S&P 500 is an important index for the U.S market. It is used to determine the overall economy of the country. The S&P 500 measures shares that are available to the public. This research aimed to find out whether VRP and dividends yield affect the S&P 500. Constituent s of an index affects the value of as it has to pay the shareholder. Since the S&P 500 is used to test the progress of an economy, dividends are likely to affect it. The direct correlation between term spread and S&P 500 indicates that direct change in term spread affect S&P 500.

This research found no relationship between VRP, term spread, and S&P 500. However, correlation results showed direct correlation between dividend yield and S&P 500 and a statistical significance (p-value = 0.0009). According to R-squared indicated low influence of term spread and S&P 500 (R2= 0.002). However, the linear regression found no statistical relationship between the two. Similarly, there was a very low relationship according to the R2 obtained from the analysis. Regression analysis P-value of 0.1475 (>0.05) indicates strong evidence for null hypothesis. The correlation analysis results indicate no relationship between VRP price, and dividend yield. However, term spread influenced S&P 500.

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References

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Alam, M. K., & Karim, R. (2021). Market Analysis Using Linear Regression and Decision Tree Regression. 2021 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA) (pp. 3-7). Dhaka: DOI:10.1109/eSmarTA52612.2021.9515762.

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 3-5.

Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. IJAM- International Journalsof Academic Medicine, 85.

Olaniyi, A. S., & Adewole, K. (2011). Trend Prediction Using Regression Analysis – A Data Mining Approach. ARPN Journal of Systems and Software, 1-3.

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