Evaluating school-based health clinics' effects on adolescent high-risk behavior

James Knickman & Paul Jellinek

Four Lessons from Evaluating Controversial Programs

Children and Youth Services Review.

The research was aimed at determining the effects of arraigning clinical services to the middle school and high school students in light of the change of the high-risk behavior as well as the utilizing the basic health care services. There were 19 sites that were established in selected schools through a non-random selection process and the observation, monitoring and evaluation was done with several adjustments made in order to see the social impact of the availability of clinical services.

What is the impact of availing basic health care services like clinics in middle school and high school on their high-risk behaviors?

BACKGROUND

A strong evaluative approach was applied in the determining of the impacts of the basic health care clinics on the behavior of the youth and also to study the variance in their behavior as the years progressed. The approach was taken in the face of the often too high expectations that are put on the evaluation process and there was need to highlight the challenges that can face the evaluation process especially in light of controversial programs as was the case here. There was a set of selected 19 schools that would be used for the study and the clinics established within those institutions. The initial evaluation intended to look at the change of social behavior of the students within the selected schools against those that were within the region and not participating in the program but this was seen to threaten the continued participation of the students. This was hence abandoned and a comparison against the national average was instead adopted.

SAMPLING

The sampling method used here was a non-random selection since there were targeted schools that the researcher wanted to have participate and evaluated. This was purposefully done to have control over the participating region and avoid variances that might arise like cultural differences, geographical diversity and population consistency that might ultimately affect the validity of the data.

INSTRUMENTATION

The data was collected by recording the number of students who used the sites and also through non-participatory observation where data on the subsequent change in behavior after the intervention by the independent variable, which is the basic care clinics within the schools.

DATA COLLECTION/ETHICS

The participation was a voluntary one hence the collection of data was also deemed to be from willing and conscious participants hence observed the ethical standards. The change in behavior was also observed in general and anonymously hence never exposed any of the participants to undue social pressure that they may have not required.

DATA ANALYSIS

The type of analysis that was used here was the exploratory data analysis method. This is the method that approach that seeks to know the previous relationship that existed and determining the changes that have been experienced in the variables (Jeffrey L., 2013). Here there was an extensive exploration of what the high-risk behaviors were before the intervention of the clinics and also an analysis of the extent of change within the schools with the sites established, relative to the national average.

AUTHOR'S CONCLUSIONS

The observation and subsequent analysis found out that there was a significant reduction in the high-risk behavior among the schools that properly utilized the sites that were established. It was concluded that the clinics improved the access to basic health care services as well as the positive impact of the services therein to the sexual behaviors of the students.

CRITIQUE

Possible Threats to Internal Validity

The first threat to internal validity is the halo effect, and this is more so bearing that it was non-random selection process, hence students from the schools could have behaved in a particular manner for the research period since they felt special and the chosen ones, or, the behavior of students in one school would influence the response to the experiment by the neighboring school. The other validity issue is the possibility of some students changing their behavior, not because they have visited the sites and understand the virtues taught therein, but because they saw their friends change, hence these participants were not influenced by the sites but by friends and yet it would be hard to tell which students fall in which category hence the internal validity, particularly in line with data analysis would be compromised.

History: the experiment report does not give a comprehensive historical perspective of the behavior of participants and the cause of this behavior and the possible variation in behavioral patterns among these participating schools, hence it is extremely difficult to tell whether this is the best intervention used and there could be no other better intervention.

Maturation: this was another aspect that would be difficult to achieve a uniform maturation of the 19 sites at the same time so that the researcher could go to evaluation of data and analysis. This could be informed by the different sub-cultures within the individual schools.

Testing: the methods that could be used for testing were the observation of the behaviors post-exposure and this was the effective way of recording data from such a controversial program.

Instrumentation: the use of observation as a way of data collection could have been influenced by the observers own bias and would observe a change that was actually not available or ignore significant change.

Regression Artifact: this is common when a sample of participants is selected from a non-random sample and in this case it could have happened bearing that the researcher chose a sample from a given predetermined population.

Selection bias: this was experienced in this experiment once the researcher decided to pick on a given population to work with, this could have also informed the perception against the population that was intentionally left out.

Experimental Mortality: this too was a threat since there must have been some of the participants who dropped out of the experiment especially with the fear of being compared to the neighboring schools hence threatening the internal validity of this experiment.

Design contamination: with the change of the evaluation design, there was a possibility that the introduction of the entire nation average could have interfered with the effective comparison since the situation within the district could have been unique in itself.

Possible Threats to External Validity

Unique program features: bearing that this program was a controversial one at the time of the experiment, there were a lot of concerns that were raised by the parents of the students being observed hence possible interference with independent observation, participation and collection of data.

Experimental arrangements: the possible threat of funding for the changed evaluation method could have significantly affected the validity of the process since the team now had to operate on a constrained budget.

Other threats: there is a possible influence from other sectors like the media since this was a controversial social issue.

CONCLUSIONS

The article is a well constructed one that highlights the social experiment and the challenges that can possibly be met in such endeavors and also how to overcome such, hence giving the reader a complete an all round article. The article is useful to any social scientist who wants to conduct similar researches and experiments and the findings herein are reliable since they were collected from actual populations. The findings here can be used to encourage the public participation in the basic heath care especially those dealing with social behavior and the issues of drug abuse. For the administrators, it is a chance to see that the social interventions that are initiated by many organizations actually bear fruits and hence the need to embrace them in their regions.

Reference

Jeffrey L., (2013). Six Types Of Analyses Every Data Scientist Should Know. Retrieved November 27, 2014 from http://datascientistinsights.com/2013/01/29/six-types-of-analyses-every-data-scientist-should-know/