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Addressing Teen Pregnancy and School Dropout in 90032 Community

Explore the alarming rates of teen pregnancy and school dropout in the 90032 community, identify risk factors, and suggest evidence-based prevention programs to mitigate these issues. Social workers can play a crucial role in implementing programs tailored to the community's needs and cultural values.

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Addressing Teen Pregnancy and School Dropout in 90032 Community

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  1. A Community Risk Assessment of 90032 Cristina A. Quiroz California State University, Long Beach May, 2012

  2. INTRODUCTION Description of problem Each year, in the United States, approximately 750,000 women, ages 15–19, become pregnant (Kost, Henshaw & Carlin, 2010). According to the Children’s Defense Fund (CDF) (2011) there are 1,240 babies who are born to teenage mothers every day. The rate of teen pregnancy in the United States remains the highest among all other industrialized nations (Chandra et al. 2008). Additionally, the California Department of Education reported that 17% of students dropped out of school in 2009-2010, which meant 91,821 students failed to graduate from high school (California Department of Education, 2011). Teen pregnancy and school dropout are inter-related as research indicates that youth who become pregnant as teenagers complete fewer grades of schooling and are more likely to drop out of school (Marteleto, Lam & Ranchhod, 2008; Pogarsky, Thornberry, & Lizotte, 2006; Dryfoos, 1990; Furstenberg et al., 1989). The rates of school dropout are higher among pregnant teens than those teens who are not pregnant and about one third of pregnant teens drop out of school either before or right after childbirth (Dogan-Ates & Carrion-Basham, 2007). Relevant Risk Factors Risk factors for school dropout and teen pregnancy are identified in the CTC model in the community, family, school and peer/individual domains. Thirteen risk factors have been found to increase the likelihood of school dropout while an increased risk of teen pregnancy is associated with 12 factors (Hawkins & Catalano, 2005).

  3. Prevention Research Risk Factor for the behaviors of concern Community Domain: Transitions and Mobility and Extreme Economic Deprivation. Family Domain: Family history of the problem behavior, family management problems and family conflict. School Domain: Academic failure beginning in late elementary school and lack of commitment to school. Peer and Individual Domain: Friends who engage in the problem behavior, favorable attitudes toward the problem behavior, early initiation of the problem behavior, early and persistent antisocial behavior and rebelliousness. Prevention Research Given the risk factors, here are some examples of evidence-based prevention approaches may prove most beneficial for the 90032 community. • The Positive Action program is a nationally recognized, evidenced-based program (SAMSHA, 2009) that could be helpful in addressing academic failure beginning in late elementary school, early initiation of the problem behavior, family history of the problem behavior, family management problems, friends who engage in the problem behavior and lack of commitment to school in the 90032 community. • The Adolescent Transitions Program (ATP) is another evidenced-based program that could help decrease risk factors in the 90032 community that are associated with transitions and mobility, family management problems, early initiation of the problem behavior and friends who engage in the problem behavior (SAMSHA, 2009).

  4. SOCIAL WORK AND CROSS CULTURAL RELEVANCE Through the implementation of these empirically-supported prevention programs, social workers may be better equipped to decrease its most prevalent risk factors associated with teen pregnancy and school dropout. The results of community risk assessments can help social workers recommend and implement suitable prevention programs that serve the most salient needs of the community. Additionally, community risk assessments should allow social workers to evaluate existing prevention programs and educate community members on the needs of the community. Social workers can also use community risk data to prioritize and prepare necessary funding proposals to help implement programs that would best serve community needs and help to mobilize community to develop a comprehensive strategy. In addition, social workers should educate community stakeholders on the importance of targeting community risk factors and using evidence-based approaches. They should also advocate for communities to organize so that a comprehensive, rather than fragmented, approach to prevention can be implemented. In addition, when working in diverse communities, social workers should try to implement programs that have been evaluated on the populations that will likely participate in the programs. If that is not possible, social workers should ensure that programs are modified to better fit with the cultural values and traditions within the community.

  5. METHODS Data Collection Procedures Using the Communities that Care Model developed by Dr. J. David Hawkins and Dr. Richard F. Catalano, professors at the University of Washington’s Social Development Research Group (Wong et al., 1996), the researcher conducted a community risk assessment of zip code 90032, in Southern California. Following the CTC model, the researcher collected information on risk factor indicators for 90032, county, state and federal areas as available. The researcher collected archival data from the most current reports available to measure indicators of each risk factor. Information included in this risk assessment was collected from national, state and local sources. Internet research and personal contacts with data sources were used to gather information. Whenever possible, the data was drawn directly on 90032 and multiple indicators were used.

  6. TRENDS AND COMPARISON DATA Extreme Economic Deprivation: The unemployment rate in 2000 for 90032 (5.8%) was slightly higher than to Los Angeles County (5.0%), and slightly higher than California (4.3%) and the United States (3.7%) (U.S. Census Bureau, 2000a). Transitions and Mobility: The percentage of renter-occupied housing units was very similar in Los Angeles County (52.3%) and 90032 (51.0%), both of which were quite a bit higher than in California (44.1%) and the United States (34.9%) (U.S. Census Bureau, 2010a, 2010b, 2010c, 2010d). Family Conflict: The percentage of couples that were divorced and separated in the year 2000. The percentage of divorced couples was similar in the United States (9.70%), California (9.50%) and Los Angeles County (8.50%) but lower in 90032 (5.90%). However, when compared to California (2.50%) and the United States (2.20%), 90032 had a slightly higher percentage of couples that were separated (3.40%), but a similar rate to Los Angeles County (3.10%) (Census Bureau, 2000b). Family Management: The rates per 1,000 children with maltreatment incidence were higher in 90032 (54.8) than in Los Angeles County (50.4) and California (49.8) (California Department of Social Services, 2010b). Family History of the Problem Behavior: The percentage of adult high school graduates for individuals 25 years of age and over. Graduation rates among adults were significantly higher in the United States (80.40%), California (76.80%) and Los Angeles County (69.90%) than in 90032 (50.50%) (U.S. Census Bureau, 2000c). Regarding teen pregnancy, the percentage of mothers giving birth to their first child between 15-19 years old was similar but slightly higher in SPA 4 (16.90%) than in California (15.90%) and Los Angeles County (15.20%) (CHIS, 2009b). Academic Failure Beginning in Late Elementary School: The percentage of fourth and fifth graders who scored below proficient in mathematics on California standardized tests. Among fourth graders, those in Los Angeles County (50%) were more likely to be below proficient than fourth graders in El Sereno Elementary in 90032 (38%), LAUSD (33%), and in California (29%). Among fifth graders, those in El Sereno Elementary in 90032 (45%) were most likely to score below proficient in math followed by LAUSD (41%) and California (37%) and Los Angeles County (37%), which were equal (California Department of Education, 2011a).

  7. DISCUSSION Community Risk Factors In this study, the community risk factors that were measured were transitions and mobility and extreme economic deprivation. The results indicated that extreme economic deprivation was the community risk factor of greatest concern for 90032. The percentage of families living below the poverty line in 90032 was higher than in Los Angeles County, substantially higher than in California and almost double that of the United States. Regarding transitions and mobility, the percentage of renter-occupied housing units was very similar in Los Angeles County and 90032, both of which were higher than in California and the United States, which suggests that children in 90032 may have an elevated risk of mobility, however, students in LAUSD moved at similar rates to the other locations. Therefore, it should prove beneficial for 90032 to implement strategies to address extreme economic deprivation and, possibly, mobility and transitions. Family Risk Factors The family risk factors assessed in this community were family conflict, family management, and family history of teen pregnancy and school dropout. The evidence indicates that family management was the risk factor of greatest concern for the 90032 community. Both the rates of child maltreatment and parental knowledge of their children’s sexual activity were much worse for 90032 than comparisons locations. Thus, the introductions of evidence-based approaches to improve family management are strongly indicted for 90032.

  8. DISCUSSION (Continued) Family history of the problem behavior, particularly school dropout, was also of concern for 90032. Although less of a challenge appeared to exist for history of teen pregnancy, it was still slightly elevated, suggesting family interventions should target this risk factor as well. Family conflicted appeared to be less of a concern, however, as rates of separation, divorce, and foster care entry were similar to or slightly lower than other locations. School Risk Factor The school risk factors measured were early academic failure and lack of commitment to school. Academic achievement in elementary school was clearly a concern for 90032 as a higher proportion of fifth grade students scored below proficient on standardized math and English/Language Arts tests than in comparison locations. Although not all of the commitment to school indicators were elevated in 90032, the school truancy rate was extremely high, so students in 90032 could potentially benefit from programs designed to increase academic achievement and school attendance at the same time. Peer and Individual Risk Factors In the peer and individual domain, the risk factors of most concern for 90032 appeared to be friends who engage in the problem behavior and early initiation of the problem behavior. The risk factors for alienation and rebelliousness, favorable attitudes toward the problem behavior, early initiation of the problem behavior, and early and persistent antisocial behavior were similar to those in comparison communities. Therefore, a focus should be placed on early intervention and prevention programs beginning in elementary school that aim to increase involvement with pro-social peers and to reduce early initiation. Overall, these findings suggest that a comprehensive intervention plan that implements strategies in all four domains could be most effective in reducing school dropout and teen pregnancy in this community.

  9. References California Department of Social Services (CDSS). (2010b). Child welfare dynamic report system university of California at Berkeley collaboration: Rates per 1,000 children with maltreatment incidence, Retrieved from http://cssr.berkeley.edu/ucb_childwelfare/GeoDataResult.aspx?report=ref10azip&county=19 Chandra, A., Martino,S., Collins, R., Elliott, M., Berry, S., Kanouse,D. & Miu, A. (2008). Does watching sex on television predict teen pregnancy? Findings from a national longitudinal survey of youth. Pediatrics, 122(5), 1047-1054. Catalano, R., Hawkins, J., Berglund, L., Pollard, J., & Arthur, M. (2002). Prevention science and positive youth development: Competitive or cooperative frameworks. Journal of Adolescent Health, 31, 230-239. Dogan-Ates, A. & Carrion-Basham, C. Y. (2007). Teenage pregnancy among Latinas: Examining risk and protective factors. Hispanic Journal of Behavioral Sciences, 29(4), 554-569. Marteleto, L., Lam, D. & Ranchhod, V. (2008). Sexual behavior, pregnancy, and schooling among young people in urban South Africa. Study of Family Planning, 39(4), 351-368. Pogarsky, G., Thornberry, T. P., & Lizotte, A. J. (2006). Developmental outcomes for children of young mothers.Journal Of Marriage & Family,68(2), 332-344. doi:10.1111/j.1741-3737.2006.00256.x Positive Action (2012). Retrieved on February 26, 2012 from, Positive Action website: http://www.positiveaction.net/research/index.asp?ID1=3&ID2=121&ID3=185 Substance Abuse and Mental Health Services Administration (SAMSHA, 2009). Retrieved from SAMSHA website: http://download.ncadi.samsha.gov/Prevline/ pdfs/ctc/CTC%20Prevention%20Strategies%20Guide%20_pdf.pdF

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