What WorX: Measuring the impact of faith-based service and social justice programs on Catholic youth

The Center for FaithJustice (CFJ) offers innovative programs that engage youth in faith, service, and social justice. With the Indiana University Lilly Family School of Philanthropy at IUPUI, they developed a survey to evaluate their programs and measure their longitudinal impact on alumni in those three focus areas. This report will offer related insights on youth engagement and suggest how CFJ’s programs relate to larger trends of youth disaffiliation within the Catholic Church. This study examines survey results from alumni and parents of alumni of CFJ’s youth programs, which are collectively called the “WorX” programs. These include curricula for middle school students (ServiceworX), high school students (JusticeworX, New Jersey Service Project/NJSP, MercyworX, and CommunityworX), young adults (LeaderworX), and adults (FaithJustice Fellows and adult volunteers). The results of this study focused on CFJ’s three core areas of interest: faith, service, and social justice

Assessing Risk Among Correctional Community Probation Populations: Predicting Reoffense With Mobile Neurocognitive Assessment Software

We seek to address current limitations of forensic risk assessments by introducing the first mobile, self-scoring, risk assessment software that relies on neurocognitive testing to predict reoffense. This assessment, run entirely on a tablet, measures decision-making via a suite of neurocognitive tests in less than 30 minutes. The software measures several cognitive and decision-making traits of the user, including impulsivity, empathy, aggression, and several other traits linked to reoffending. Our analysis measured whether this assessment successfully predicted recidivism by testing probationers in a large urban city (Houston, TX, United States) from 2017 to 2019. To determine predictive validity, we used machine learning to yield cross-validated receiver–operator characteristics. Results gave a recidivism prediction value of 0.70, making it comparable to commonly used risk assessments. This novel approach diverges from traditional self-reporting, interview-based, and criminal-records-based approaches, and can also add a protective layer against bias, while strengthening model accuracy in predicting reoffense. In addition, subjectivity is eliminated and time-consuming administrative efforts are reduced. With continued data collection, this approach opens the possibility of identifying different levels of recidivism risk, by crime type, for any age, or gender, and seeks to steer individuals appropriately toward rehabilitative programs. Suggestions for future research directions are provided

Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices

Drug-resistant epilepsy (DRE) is often treated with surgery or neuromodulation. Specifically, responsive neurostimulation (RNS) is a widely used therapy that is programmed to detect abnormal brain activity and intervene with tailored stimulation. Despite the success of RNS, some patients require further interventions. However, having an RNS device in situ is a hindrance to the performance of neuroimaging techniques. Magnetoencephalography (MEG), a non-invasive neurophysiologic and functional imaging technique, aids epilepsy assessment and surgery planning. MEG performed post-RNS is complicated by signal distortions. This study proposes an independent component analysis (ICA)-based approach to enhance MEG signal quality, facilitating improved assessment for epilepsy patients with implanted RNS devices. Three epilepsy patients, two with RNS implants and one without, underwent MEG scans. Preprocessing included temporal signal space separation (tSSS) and an automated ICA-based approach with MNE-Python. Power spectral density (PSD) and signal-to-noise ratio (SNR) were analyzed, and MEG dipole analysis was conducted using single equivalent current dipole (SECD) modeling. The ICA-based noise removal preprocessing method substantially improved the signal-to-noise ratio (SNR) for MEG data from epilepsy patients with implanted RNS devices. Qualitative assessment confirmed enhanced signal readability and improved MEG dipole analysis. ICA-based processing markedly enhanced MEG data quality in RNS patients, emphasizing its clinical relevance

Diagnostic accuracy of tablet-based software for the detection of concussion - Fig 4

<p><b>Performance on BrainCheck assessments by (A) gender and (B) socioeconomic status.</b> As explained in the text, assessments performed at different university test sites were used as a rough measure of the effect of socioeconomic status. Error bars represent standard error of the mean, computed by bootstrapping.</p

Test-retest reliability.

<p>In all panels, each datapoint represents an individual who took the same assessment on two different dates. Black lines represent linear fits to the data. r-values for the fits are shown in the legend of each panel.</p

Diagnostic accuracy of tablet-based software for the detection of concussion - Fig 4

<p><b>Performance on BrainCheck assessments by (A) gender and (B) socioeconomic status.</b> As explained in the text, assessments performed at different university test sites were used as a rough measure of the effect of socioeconomic status. Error bars represent standard error of the mean, computed by bootstrapping.</p

Individual metrics differ for concussed and healthy individuals.

<p>For each assessment, normative histograms for healthy (blue) or concussed (red) individuals are shown.</p

A test for malingering shows no dependence on cognitive performance.

<p>(A) Screenshot of the malingering test. (B) Normative data for the test (C) Scores on the malingering test plotted by age. (D) Data for individuals who took the malingering test twice separated by at least one week. The score on the first trial is plotted against the score on the second. (E) Distribution of scores on the malingering test for healthy (blue) and concussed (red) individuals. (F) Specificity (blue) and sensitivity (red) of a test to distinguish concussed and healthy individuals based on the malingering test.</p

Demographics of the study populations at the different types of test sites and for the different study populations.

<p>Demographics of the study populations at the different types of test sites and for the different study populations.</p