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This study examines patient care in the SHOW free clinic in Phoenix, Arizona, which serves adults experiencing homelessness. This study asks two questions: First, do clinicians in Phoenix’s SHOW free clinic discuss with patients how to pay for and where to access follow-up services and medications? Second, how do the backgrounds of patients, measured by scales based on the Gelberg-Anderson behavioral model for vulnerable populations, correlate with patient outcomes, including number of unmet needs in clinic, patient satisfaction with care, and patient perceived health status? To answer these questions, structured surveys were administered to SHOW clinic patients at the end of their visits. Results were analyzed using Pearson’s correlations and odds ratios. 21 patients completed the survey over four weeks in February-March 2017. We did not identify any statistically significant correlations between predisposing factors such as severity/duration of homelessness, mental health history, ethnicity, or LGBTQ status and quality of care outcomes. Twenty nine percent of surveyed patients reported having one or more unmet needs following their SHOW clinic visit suggesting an important area for future research. The results from this study indicate that measuring unmet needs is a feasible alternative to patient satisfaction surveys for assessing quality of care in student-run free clinics for homeless populations.
The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.
My proposed project is an educational application that will seek to simplify the<br/>process of internalizing the chord symbols most commonly seen by those learning<br/>musical improvisation. The application will operate like a game, encouraging the<br/>user to identify chord tones within time limits and award points for successfully<br/>doing so.