An Outcome Evaluation Of The SOS Suicide Prevention Program

In the United States, suicide is the third leading cause of death among persons between the ages of 10-24 [1]. In addition, a large percentage of adolescents consider committing suicide. The Centers for Disease Control found that 16% of students in high schools nationwide reported seriously considering suicide, while 8 percent reported trying to take their own life [1]. One of the difficulties in addressing the issue of suicide is the stigma attached to it. People are unwilling to discuss it publically. [1]

A valuable step in any policy process is evaluation. A S.W.O.T. analysis examines the strengths, weaknesses, opportunities, and threats of any program. This can be powerful tool for policy and program evaluation as it is designed to identify any areas for improvement or those that are sustainable (Milstead, 2016). Using the S.W.O.T. analysis for suicide prevention is used as a beginning framework for evaluation (Appendix).

One of the major issues of the 21st century is suicide. The suicide rate has been increasing over the years rather than decreasing currently, “Suicide is the second leading cause of death for people15-29 years of age. Teen suicide statistics for youths 15-19 years of age indicate that from 1950-1990, the frequency of suicides increased by 300% and from 1990-2003, than that rate decreased by 35%.Over the past decade, however, the rate has again increased to 12.1 per 100,000. Every day, approximately 105 Americans die by suicide” (Suicide Fact 2003-2016). Suicide is not something easy that can be controled because although it is possible to see when a person has a problem it is hard to get the person to speak about the situation they are in.

The Youth suicide prevention program put out statistics saying, 2001 in the US, 4,250 young people between the ages of ten and twenty four died by suicide, which is an average of one suicide roughly every two hours. They go on to say that one out of ten students indicated that they had made a suicide attempt in the past twelve months. One out of five indicated that they had seriously thought about it. Teen suicide is a bigger problem than people realize with rates of suicide tripling since 1970. It is an ongoing problem that seems to keep growing. Young people all across America choose to end their own lives, but could it be preventable? Brian Ahmedani studied suicide attempts across the United States stated that, sixty four percent of people

Teen suicide is a plague on our nation that has claimed thousands of lives, yet could be easily prevented through the recognition of warning signs and proper education. The number of cancer, lung disease, heart disease, stroke, pneumonia, influenza, AIDS, and birth defect related deaths combined does not amount to the number of deaths caused by suicide in young adults and teenagers. In the majority of cases, teens considering suicide showed warning signs. Four out of five teenagers who have attempted suicide showed warning signs of suicidal thoughts, yet little to no effort has been put in to put these students back on track (The Jason Foundation). Making proper resources required in school curriculum could guide struggling students towards a better life. If schools put in the effort to identify students who show signs of suicide and depression, countless lives would not be lost.

Suicide causes, etiologies, and prevention strategies were examined in Mesa county of western Colorado. The highest population at risk for completion of suicide within Mesa county and that of the national average were found to be White males between the ages of 36-54 years of age. Risk factors of suicide and demographics of this population were assessed and compared to the efficacy of current suicide programs utilized within Mesa county. Data was then compared from Mesa county’s demographics, suicide rates, and prevention programs to current literature and the District of Columbia, which hosts the lowest suicide rate in the country. The juxtaposition of current literature and the District of Columbia’s suicide prevention programs to Mesa county’s current suicide prevention program were made to identify the efficacy and areas of improvement needed to further reduce suicide rates.

My SLEP action plan is designed to educate the students of Boaz High School Health Science Department on the causes, signs and prevention of suicide attempts. I plan to make a pamphlet to hand out to all students who are in the health science classes. The pamphlets will include information for adolescents age 14-18 about suicide detection and prevention. When I met with the health science teacher on 1-26-17, she expressed that mental health, specifically

Checking whether or not inference about treatment effect may be changed by unobserved (hidden bias) factors or deviation of the estimated result from assumptions is very important and

The fixed-effect model (equation (1)) assumes that the k study-specific summary statistics share a common mean θ. A statistical test for the homogeneity of study means is equivalent to testing

As observed by the Journal of American College Health and Rivero, E.M. et al (2014), “90% of all individuals who die by suicide … have a diagnosable psychiatric condition … [and] although approximately 1,100 college students die by suicide each year, fewer than 20% of students … were receiving mental health treatment.” The journal goes on to state that those who were receiving professional treatment were less likely to attempt suicide, along with the students who had a strong support group of family and friends. By implementing a program designed to intervene and provide aid before a suicide attempt can be made, the hopes of a positive outcome and may increase the amount of students who succeed and graduate from college.

Regarding public health, suicide has and continues to be a major issue, accounting for 40,000 deaths per year within the general population of the United States alone (cite). While the devastating effects of suicide impacts the entire population, what is even more alarming is the epidemic of death by suicide within the college student population, with this age cohort at a dramatically higher risk for suicide when compared to the public. Therefore, it is unfortunately not surprising that death by suicide is noted as the second leading cause of death for the college student demographic, yielding approximately 1,100 deaths each year (cite). Similarly to the general population, there are at-risk groups within the college community that place individuals at a higher risk of suicidal behaviors such as ideation and attempts (cite). Some of these at-risk groups include LGBT, first generation, and international students (cite). Additionally, there are environmental factors that college students will experience through this transitional period that will also contribute to this demographic being at a higher risk of suicide.

Question 3. Ӯ=b+mx or Ӯ=mx+b, Ӯ= dependent variableoverall, a= constant b, b1=predictor 1GRE score on quantitative b value, x1 = GRE score on quantitative. b2=predictor 2GRE score on verbal b value, x2=GRE score on verbal. B3=predictor 3ability to interact easily b value, x3=ability to interact easily. Equation- Ӯ=a+b1(x1) +b2(x2) +b3(x3)Overall college GPA=2.250+0.002(GRE, quantitative+0.028(ability to interact). Step 1-If the model is significant with a significant value of 0.014, less than 0.05. High F value (3.907), lower significance value (.014). Step 2=Amounted accounted for=R2=.20320.3% of the variance is accounted for by the predictors. There was a moderate effect size. There is a moderate correlation (R=0.451)

As research across all fields and topics evolve, techniques on how to improve research endeavors, such as statistical modeling techniques, become important to utilize. Hierarchical linear modeling, similarly known as multilevel modeling, has been a statistical approach that has gained attention and improved the analysis and interpretation of research data (Osborne, 2000). Hierarchical linear modeling is a regression-based statistical analysis that considers the hierarchical (i.e., multiple levels; nested) nature of variables within a dataset. Hierarchical data is data that has an organizational structure consisting of units of groups (i.e., levels) that are clustered under a larger group. Therefore, the variables within a dataset may have mediating effects and such effects can only be explored when taking into account the clustering of groups in the data.

Models (GLM) to data from simple random and complex survey designs. This program supports a range of sampling distributions including Gaussian, inverse Gaussian, multinomial, binomial, negative binomial, Bernoulli, Poisson, and gamma. The last program, MAPGLIM can be used to fit GLM models to the multilevel data. Each of these programs can function as stand-alone program.

The data was made up of a random sample of 150 Faber College day students. The data recorded was each student’s gender they identified with, ethnicity, class level, high school GPA,