Matching Items (2)
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With a prison population that has grown to 1.4 million, an imprisonment rate of 419 per 100,000 U.S. residents, and a recidivism rate of 52.2% for males and 36.4% for females, the United States is facing a crisis. Currently, no sufficient measures have been taken by the United States to

With a prison population that has grown to 1.4 million, an imprisonment rate of 419 per 100,000 U.S. residents, and a recidivism rate of 52.2% for males and 36.4% for females, the United States is facing a crisis. Currently, no sufficient measures have been taken by the United States to reduce recidivism. Attempts have been made, but they ultimately failed. Recently, however, there has been an increase in experimentation with the concept of teaching inmates basic computer skills to reduce recidivism. As labor becomes increasingly digitized, it becomes more difficult for inmates who spent a certain period away from technology to adapt and find employment. At the bare minimum, anybody entering the workforce must know how to use a computer and other technological appliances, even in the lowest-paid positions. By incorporating basic computer skills and coding educational programs within prisons, this issue can be addressed, since inmates would be better equipped to take on a more technologically advanced labor market.<br/>Additionally, thoroughly preparing inmates for employment is a necessity because it has been proven to reduce recidivism. Prisons typically have some work programs; however, these programs are typically outdated and prepare inmates for fields that may represent a difficult employment market moving forward. On the other hand, preparing inmates for tech-related fields of work is proving to be successful in the early stages of experimentation. A reason for this success is the growing demand. According to the U.S. Bureau of Labor Statistics, employment in computer and information technology occupations is projected to grow 11 percent between 2019 and 2029. This is noteworthy considering the national average for growth of all other jobs is only 4 percent. It also warrants the exploration of educating coders because software developers, in particular, have an expected growth rate of 22 percent between 2019 and 2029. <br/>Despite the security risks of giving inmates access to computers, the implementation of basic computer skills and coding in prisons should be explored further. Programs that give inmates access to a computing education already exist. The only issue with these programs is their scarcity. However, this is to no fault of their own, considering the complex nature and costs of running such a program. Accordingly, this leaves the opportunity for public universities to get involved. Public universities serve as perfect hosts because they are fully capable of leveraging the resources already available to them. Arizona State University, in particular, is a more than ideal candidate to spearhead such a program and serve as a model for other public universities to follow. Arizona State University (ASU) is already educating inmates in local Arizona prisons on subjects such as math and English through their PEP (Prison Education Programming) program.<br/>This thesis will focus on Arizona specifically and why this would benefit the state. It will also explain why Arizona State University is the perfect candidate to spearhead this kind of program. Additionally, it will also discuss why recidivism is detrimental and the reasons why formerly incarcerated individuals re-offend. Furthermore, it will also explore the current measures being taken in Arizona and their limitations. Finally, it will provide evidence for why programs like these tend to succeed and serve as a proposal to Arizona State University to create its own program using the provided framework in this thesis.

ContributorsAwawdeh, Bajis Tariq (Author) / Halavais, Alexander (Thesis director) / Funk, Kendall (Committee member) / School of Social and Behavioral Sciences (Contributor, Contributor) / School of Humanities, Arts, and Cultural Studies (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
Introduction: There is currently a lack of industry-wide gold standardization in accelerometer study
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with
polysomnography (PSG) corroboration. An existing algorithm used to differentiate

Introduction: There is currently a lack of industry-wide gold standardization in accelerometer study
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with
polysomnography (PSG) corroboration. An existing algorithm used to differentiate valid/invalid wear
time and detect bouts of sleep has been modified with the goal of maximizing accuracy of sleep bout
detection. Methods: Three key decisions and thresholds of the algorithm have been modified with three
experimental values each being tested. The main experimental variable Sleepwindow controls the
amount of time before and after a determined bout of sleep that is searched for additional sedentary
time to incorporate and consider part of the same sleep bout. Results were compared to PSG and sleep
diary data for absolute agreement of sleep bout start time (START), end time (END) and time in bed
(TIB). Adjustments were made for outliers as well as sleep latency, snooze time, and the sum of both.
Results: Only adjustments made to a sleep window variable yielded altered results. Between a 5-, 15-,
and 30-minute window, a 15-minute window incurred the least error and most agreement to
comparisons for START, while a 5-minute window was best for END and TIB. Discussion: Contrary
to expectation, corrections for snooze, latency, and both did not substantially improve agreement to
PSG. Algorithm-derived estimates of START and END always fell after sleep diary and PSG both,
suggesting either participants’ sedentary behavior beginning and ends were at a delay from sleep and
wake times, or the algorithm estimates consistently later times than appropriate. The inclusion of a
sleep window variable yields substantial variety in results. A 15-minute window appears best at
determining START while a 5-minute window appears best for END and TIB. Further investigation on
the optimal window length per demographic and condition is required.
ContributorsMartin, Logan Rhett (Author) / Buman, Matthew (Thesis director) / Toledo, Meynard John (Committee member) / Kurka, Jonathan (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12