Methods: The Eye to Eye mentorship program assessed involved mentors and mentees who completed 12 in-person art sessions out of the normal 20 in-person sessions. The first main assessment was the BLD (Breger Learning Difference) Feedback Survey addressing one’s experience in the Eye to Eye program and which were completed at the end of the mentorship program and filled out by mentors, mentees, and mentees parents (one parent for each mentee). A total of 12 mentors, 6 mentees, and 6 mentee parents were included in the feedback survey final analysis. The second main assessments were the pre and post Behavior Assessment System for Child, Third Edition (BASC-3) provided to mentors, mentees, and mentees parents (one parent for each mentee). A total of 10 mentors, 5 mentees, and 5 mentee parents were included in pre and post BASC-3 final analysis. Fall 2019 (pre) and Spring 2020 (post) optional interviews involved 5 mentors and 3 mentees who showed interest and were comfortable participating with additional release forms.
Results: The program was generally positively rated in the feedback survey by mentors, mentees, and mentee parents. The highest responses for mentors, mentees, and mentee parents all incorporated average ratings of 4.0 or higher (out of 5.0) for perceived understanding of socio-emotional skills after Eye to Eye, experience in Eye to Eye, how having a mentor or mentee made them feel, and perceived change in self-awareness. All three groups reported fairly high ratings of improved self-awareness of 4.0/5.0 or above. No negative ratings were provided by any participants and the lowest response was no change. The BASC-3 evaluation found statistically significant improvement in mentors’ anxiety and atypicality and mentees’ sense of inadequacy.
Discussion: The Eye to Eye program was popular and well-rated despite only involving 12 in- person one-hour art sessions. The mentors, mentees, and mentee parents felt positive about the Eye to Eye program when answering the feedback survey. Some suggestions are made on how to improve this program to better enhance someone with learning differences future ability to succeed. Future research is needed to assess the true impact due to the COVID-19 epidemic and other limitations.
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The combined use of methamphetamine and opioids has been reported to be on the rise throughout the United States (U.S.). However, our knowledge of this phenomenon is largely based upon reported overdoses and overdose-related deaths, law enforcement seizures, and drug treatment records; data that are often slow, restricted, and only track a portion of the population participating in drug consumption activities. As an alternative, wastewater-based epidemiology (WBE) has the capability to track licit and illicit drug trends within an entire community, at a low cost and in near real-time, while providing anonymity to those contributing to the sewer shed. In this study, wastewater was collected from two Midwestern U.S. cities (2017-2019) and analyzed for the prevalence of methamphetamine and the opioids oxycodone, codeine, fentanyl, tramadol, hydrocodone, and hydromorphone. Monthly 24-hour time-weighted composite samples (n = 48) from each city were analyzed using isotope dilution liquid chromatography tandem mass spectrometry. Results showed that methamphetamine and total opioid consumption (milligram morphine equivalents) in City 1 were strongly correlated only in 2017 (Spearman rank order correlation coefficient, ρ = 0.78), the relationship driven by fentanyl, hydrocodone, and hydromorphone. For City 2, methamphetamine and total opioid consumption were strongly positively correlated during the entire study (ρ = 0.54), with the correlations driven by hydrocodone and hydromorphone. In both cities, hydrocodone and hydromorphone mass loads were highly correlated, suggesting a parent and metabolite relationship. WBE provides important insights into licit and illicit drug consumption patterns in near real-time as they evolve; important information for community stakeholders in municipalities across the U.S.
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GitHub Repository: https://github.com/komal-agrawal/AD_GIS.git
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