Analyzing the Aggregate Strengths and Weaknesses

When comparing the outcomes of a community’s healthcare systems, it would be clear that each aggregate has its strengths and weaknesses. For example, in one community the rate of teen pregnancies may be high while in another tornadoes and flooding issues may be the weak factor of that community. Another community may have low rates of unemployment but high rates of cardiovascular issues due to the lifestyles they lead. This, therefore, means that communities have to improve their general, efficiency, and quality performance to deal with such outcomes.

Among the strengths of the aggregate in the community include the ability of a researcher to detect small but clinically relevant impacts of an intervention. Further, one can avoid the expense and effort involved in undertaking a large confirmatory study since statistical evidence from various studies may be available regarding the outcomes in the community where the aggregate resides. One is also able to statistically examine the heterogeneity and bias of the statistical tools and thus measure the sensitivity of the statistically significant results (Anderson & McFarlane, 2011).

The weaknesses, on the other hand, may include bias data as well as inconsistent coding of variables which may lead to limitations while using them to detect heterogeneity. Moreover, unless the data collected in the community is consistent across all the studies done, it may be difficult to detect any interaction and trends. Studies must show a decline or decrease in teen pregnancies in the community and should have consistencies which show the accuracy of the data. If there are any severe heterogeneity detected, its source may be hard to determine.

After looking at the trend data in the community, one would notice that the rates of teen pregnancies, tornadoes, and flooding, and unemployment rates have not decreased in comparison to other regions. A review of the national data shows that the community is below target forcing one to conduct an environmental scan to see what practices would lower these rates. Stakeholders would thus be brought forth to craft a plan. The task force would then conduct strategic planning meetings to look into the data on teen pregnancies, flooding, and unemployment rates. After a review of the data, it is found that the cases of teen pregnancies have increased by 6.2 percent while the unemployment rate is at 11.9 percent. The lead prevention plans of these outcomes may be used in addressing the causes of the problems.

A pilot program would then be rolled out to educate teenagers on sex issues and dangers to reduce the rates of teen pregnancies. It would also outline some of the entrepreneurial activities the unemployed population may engage in. When implementing the findings, training would be essential to ensure that the project succeeds with regard to the number of sex educators and people tasked with building water barriers in case of flooding. Lastly, the team would then need to track the progress of the project on whether the project leads to a reduction in the outcomes.

The major goal of the process was to work with the families during the whole project. The data collected were based on perceptions and experiences of the family members to provide the risk factors regarding teen pregnancies. Data collected from 12 members in the two families was analyzed for this report by trained interviewers on a door-to-door basis. The risk factors selected and analyzed as predictors of teen pregnancies were individual, family, school, and peer influenced. The risk factors included early and persistent problem behavior, lack of commitment to school, weak social ties, and family management problems (Hawkins, Catalano & Kuklinski, 2011). The project would be expected to reduce teen pregnancies by 60 percent through education and improvement of family ties. However, teenagers engaging in problem behavior would be used as a strong predictor of the threat of teenage pregnancies between the ages of 13 and 19.

 

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