Race, Employment Disadvantages, and Heavy Drinking: A Multilevel Model

Motivation Analysis of Technological Startups Business Models Based on Intelligent Data Mining and Analysis

To improve the motivation analysis effect of the technological startups business model, this study combines intelligent data mining technology to analyze related factors and proposes a method of adjusting parameters of machine-learning model based on Bayesian optimization algorithm. Moreover, this study integrates the prior information and sample information of the machine learning algorithm performance function and builds an intelligent analysis model based on the elements of the technological startups business model. Finally, this article designs experiments to evaluate the effect of intelligent data mining of the system model in this study. The research shows that the motivation analysis model of the technological startups’ business model based on intelligent data mining analysis proposed in this study meets the basic requirements of the design system of this study.

Research on the impact of entrepreneurship policy on employment based on improved machine learning algorithms

After my country’s economy has entered a new normal, in terms of employment, which has led to the coexistence of the old and new contradictions in employment in our country and the coexistence of employment expansion and stabilization of employment. In this context, it is impossible to achieve full employment and completely eliminate unemployment by relying solely on economic growth. This paper improves traditional machine learning algorithms and builds an entrepreneurial policy analysis model based on improved machine learning to analyze the impact of entrepreneurial policies on employment. Moreover, this paper uses a projection pursuit comprehensive evaluation model optimized by genetic algorithm to conduct empirical research on entrepreneurial environment conditions. In addition, this paper verifies its rationality by regression analysis of empirical results and TEA (Entrepreneurial Activity of All Employees) index, and deeply explores the inherent laws and development characteristics of entrepreneurial environmental conditions from multiple perspectives such as time series and spatial distribution. The research results show that the method proposed in this paper is effective.

Racial discrimination and alcohol use and negative drinking consequences among Black Americans: a meta-analytical review

BACKGROUND AND AIMS

Findings of the association between racial discrimination and alcohol use and related consequences are inconsistent, and the role of potential moderators in the association is largely unknown. This meta-analysis aimed to synthesize the discrimination-alcohol literature among Black Americans, estimate the magnitude of associations and explore differences as a function of sample characteristics.

METHODS

Empirical studies reporting the association of racial discrimination with alcohol-related behaviors in an all-black sample were identified via systematic literature search. A random-effects meta-analysis was conducted using 33 effect sizes extracted from 27 studies, all of which used US samples (n = 26 894).

RESULTS

Significant positive associations were found for racial discrimination with alcohol consumption [k = 9, confidence interval (CI) = 0.08, 0.17, I²  = 49%, r = 0.12], heavy/binge drinking (k = 12, CI = 0.02, 0.10), I²  = 27%, r = 0.06), at-risk drinking (k = 4, CI = 0.06, 0.23, I²  = 0%, r = 0.14) and negative drinking consequences (k = 5, CI = 0.09, 0.25, I²  = 94%, r = 0.25), but not with alcohol use disorder (k = 3, CI = -0.01, 0.20, I²  = 90%, r = 0.10). Only alcohol consumption and negative drinking consequences showed significant between-study heterogeneity and had a sufficient quantity of studies for moderation analysis (i.e., 4 or more studies). The positive association of racial discrimination with negative drinking consequences was stronger among younger samples; the association with alcohol consumption did not differ by age or proportion of men.

CONCLUSIONS

Experiences of racial discrimination are associated with diverse alcohol-related behaviors among Black Americans, with a stronger association with problematic alcohol use, particularly among younger individuals.

Connecting the Dots: State Health Department Approaches to Addressing Shared Risk and Protective Factors Across Multiple Forms of Violence

Violence takes many forms, including intimate partner violence, sexual violence, child abuse and neglect, bullying, suicidal behavior, and elder abuse and neglect. These forms of violence are interconnected and often share the same root causes. They can also co-occur together in families and communities and can happen at the same time or at different stages of life. Often, due to a variety of factors, separate, "siloed" approaches are used to address each form of violence. However, understanding and implementing approaches that prevent and address the overlapping root causes of violence (risk factors) and promote factors that increase the resilience of people and communities (protective factors) can help practitioners more effectively and efficiently use limited resources to prevent multiple forms of violence and save lives. This article presents approaches used by 2 state health departments, the Maryland Department of Health and Mental Hygiene and the Colorado Department of Public Health and Environment, to integrate a shared risk and protective factor approach into their violence prevention work and identifies key lessons learned that may serve to inform crosscutting violence prevention efforts in other states.

Employment after beginning treatment for substance use disorders: The impact of race/ethnicity and client community of residence

Employment is an important substance use treatment outcome, frequently used to assess individual progress during and after treatment. This study examined whether racial/ethnic disparities exist in employment after beginning treatment. It also examined the extent to which characteristics of clients' communities account for such disparities. Analyses are based on data that linked individual treatment information from Washington State's Behavioral Health Administration with employment data from the state's Employment Security Department. Analyses subsequently incorporated community-level data from the U.S. Census Bureau. The sample includes 10,636 adult clients (Whites, 68%; American Indians, 13%, Latinos, 10%; and Blacks, 8%) who had a new outpatient treatment admission to state-funded specialty treatment. Heckman models were used to test whether racial/ethnic disparities existed in the likelihood of post-admission employment, as well as employment duration and wages earned. Results indicated that there were no racial/ethnic disparities in the likelihood of employment in the year following treatment admission. However, compared to White clients, American Indian and Black clients had significantly shorter lengths of employment and Black clients had significantly lower wages. With few exceptions, residential community characteristics were associated with being employed after initiating treatment, but not with maintaining employment or with wages. After accounting for community-level variables, disparities in length of employment and earned wages persisted. These findings highlight the importance of considering the race/ethnicity of a client when examining post-treatment employment alongside community characteristics, and suggest that the effect of race/ethnicity and community characteristics on post-treatment employment may differ based on the stage of the employment process.

Job loss and alcohol dependence among Blacks and Whites in a National Longitudinal Survey

We used the 1979 National Longitudinal Survey of Youth to test whether the association between job loss and incidence of alcohol dependence differed across Blacks and Whites. Respondents were interviewed annually from 1979 to 1994; DSM-IV dependence was assessed in 1989 and 1994. Analyses included only those employed in 1989 and involved lagged logistic regressions predicting past-year dependence in 1994 from job loss during 1990-1993. Unexpectedly, results showed stronger and more robust associations between job loss and dependence among Whites (AOR = 1.93, p < .05) than among Blacks (AOR = 0.82, nonsignificant). Findings diverge from prior research, suggesting disparities may differ as a function of age and/or time.