Introduction
The objective of the study is to find the correlations of the sociodemographic public stigma while putting into consideration life satisfaction and mental health knowledge (literacy). Public stigma is the universal attitude that the members of the public have regarding individuals having mental illnesses (Lo et al., 2021). The research also investigates the population subgroup that will benefit from the destigmatizing programs that aim to enhance mental health literacy. Public stigma is associated with mental health conditions and negatively impacts the general well-being of individuals because it prevents them from seeking help. Many researchers have shown the impact of destigmatizing programs on mental illnesses (Lo et al., 2021). The programs are usually used to target specific groups, such as professionals (teachers and healthcare personnel).
The programs have proven effective on a short-term basis with successful results on some individuals. The study focus on the general public, as many people suffering from mental illnesses, have suffered significant discrimination (Lo et al., 2021). Creating education programs for awareness is a significant factor in ensuring that the attitudes regarding this group are changed to enhance their wellness and mental condition. The study considers the essentiality of understanding various sociodemographic groups regarding mental illness stigma and public knowledge.
Hypothesis
- Ho: Mental health literacy does not significantly predict public stigma (Bi = 0)
- Ha: Mental health literacy does significantly predict public stigma (Bi ≠ 0)
Variables
The independent variable in the research study is mental health literacy, dichotomous variables retired, not retired, gender (male or female), and house maker (house makers and non-housemakers). The continuous independent variables include age and life satisfaction, while the dependent variable is the public stigma.
Statistical Techniques
Several techniques were utilized in the analysis of the research data. These techniques include descriptive statistics, internal reliability, multiple linear regression, and moderation analysis.
Descriptive statistics
Descriptive statistics were used to describe the random sample of size 1514. The variable investigated include; gender, age group, education, and occupation. From table 1.0, the gender recorded was only male and female, with the male population of 719 representing 47.5% and the female population of 795 representing 52.5% of the overall population (Lo et al., 2021). The age group recorded was 18-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, and above 60 years. The population composition of the age group was 2.3%, 7.4%, 7.8%, 5.9%, 9.6%, 7.9%, 10.5%, 9.3%, 7.7%, and 31.2%, respectively (Lo et al., 2021). The education recorded was primary or below, representing 19.8%; secondary representing 47.4%, and tertiary representing 32.8% (Lo et al., 2021). The occupations recorded were mid-level, senior management, retired, homemakers, students, and others. The percentage composition of the occupation is 38.0%, 16.0%, 5.9%, 18.4%, 10.4%, 4.8%, and 6.5%, respectively (Lo et al., 2021). The descriptive for Reported and Intended Behavior Scale (RIBS), Mental Health Knowledge Schedule (MAKS), and life satisfaction are MAKS (mean = 9.74, S.D = 9.00), RIBS (mean = 3.99, S.D = 1.32), and life satisfaction (mean = 7.03, S.D = 1.81) (Lo et al., 2021). The descriptive shows that the old was the most prominent group in the study.
Reliability Analysis
The internal reliability was tested for the scale score used to measure public stigma. The public stigma score used the Reported and Intended Behavior Scale (RIBS), whose measures were a five-point Likert scale (Lo et al., 2021). The performance score of the RIBS scale was analyzed in that a high score on the RIBS scale portrays a high level of public stigma on mental health illness. Cronbach’s Alpha was used to measure internal reliability (Lo et al., 2021). The reliability score was 0.85 and deemed sufficient since it was above 0.60.
Multiple Linear Regression
Multiple linear regression was used to analyze SES variables and determine if they were significant predictors of public stigma. The article identifies the SES variables as “correlates,” which indicates they were already known to have a linear relationship or correlation with public stigma. This is the first reason why multiple linear regression analysis was appropriate (Lo et al., 2021). The second reason is that the researchers wanted to account for mental health literacy and life satisfaction in the analysis. Regression analysis allows for analyzing the predictive effect of a variable on a dependent variable while controlling for all other variables in the analysis.
The multiple regression in this research study used control variables such as mental health literacy and life satisfaction. The p-values and Beta values from table 2.0 are; female gender (β = 0.054, p =.03), older age groups (β = 0.108, p =.003), retirement (β = 0.179, p <.001) (Lo et al., 2021). From the p-values above, it can be noted that the values are less than 0.05, which implies that the variables are significant in predicting the outcome of the mental health illness stigma (Liu et al., 2021). The coefficient of determination of the model (R2 = 0.164). This implies that 16.4% of the variation in mental health illness stigma can be explained by the combined variation of the female gender, older age group, and retirement.
Moderation Analysis
Moderation analysis is a multiple regression analysis that analyzes the effect of a moderator variable on a linear relationship between an independent variable (IV) and a dependent variable (DV). The moderating variable is the interaction between a variable and another IV (Lo et al., 2021). In this way, moderation analysis was appropriate for this research because the interaction between age and education and its effect on public stigma was part of the research objective for this study.
From table 3.0 moderation results, education and age differences portrayed a significant association in group 2, composed of old with lower education. This was followed by group 3, composed of the old and with high education (Lo et al., 2021). The liner results are (Group 2: β = 0.672, p <.001; Group 3: β = 0.292, p <.001 (Lo et al., 2021). The p-values for the two groups are less than 0.01, indicating the significance of the results. The coefficient of determination for this group was R2 = 0.149 (Lo et al., 2021). This implied 14.9% of the variation in mental health stigma could be explained by the combined variation of group 2 and group 3 (Lo et al., 2021). Figure 1.0 portrays the interaction effect that exists between the main variables of the study (mental health literacy and public stigma). Group 2 had β = − 0.454, p <.001 and group 3 had β = − 0.308, p <.001 (Lo et al., 2021). This shows that groups 2 and 3 strongly correlate with public stigma and mental health knowledge. The p-value shows that the interaction is significant on participants’ age and level of education.
The Blue Assumptions
Firstly, the assumption of multicollinearity in the study is met as there is no multicollinearity between the study variables. The assumption focuses on the linear dependence of the study’s independent variables. In this study, the linear variables are independent, making it significant to employ the multiple linear regression method (Du et al., 2020). Secondly, the covariance assumption between the independent and residual variables is also met. The research study variable between the independent and the residual is zero because of the lack of spurious relations. Thirdly, the normality assumption where the error terms are uncorrelated and independent is met. The multiple regression model used involves independent and independent variables (Lo et al., 2021). Lastly, the model assumption of random variables is fulfilled as the mean of the error term is zero with a constant variance.
Conclusion
The research study examined the public stigma of mental health using the sociodemographic data gathered from the Hong Kong population. The linear regression analysis results show a correlation between public stigma and mental illness knowledge. The analysis findings show that females, older people, and individuals having lower education levels have a high chance of stigma from mental illness. The difference in generational culture provides potential information regarding age and mental illness stigma among the old population. The study’s outcome is well supported by the coefficient of determination results. The coefficient of determination has significantly shown that mental health stigma is dependent on education level, gender, and age. Despite the lack of evidence to ascertain the stigma of various occupations in society, the results show that homemakers and retired individuals have a higher chance of public stigma. The strength of the association between mental health literacy and public stigma varies depending on the level of education and age. Older people with low education levels require destigmatizing programs because they are the most vulnerable.
References
Du, Z., Hu, Y., & Buttar, N. (2020). Analysis of mechanical properties for tea stem using grey relational analysis coupled with multiple linear regression. Scientia Horticulturae, 260, 108.
Liu, M., Hu, S., Ge, Y., Heuvelink, G., Ren, Z., & Huang, X. (2021). Using multiple linear regression and random forests to identify spatial poverty determinants in rural China. Spatial Statistics, 42, 100.
Lo, L. L., Suen, Y. N., Chan, S. K., Sum, M. Y., Charlton, C., Hui, C. L., Lee, E. H., Chang, W. C., & Chen, E. Y. (2021). Sociodemographic correlates of public stigma about mental illness: A population study on Hong Kong’s Chinese population. BMC Psychiatry, 21(1).