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- Creators: College of Health Solutions
- Creators: Chamberlain, Alyssa
Background: Creation and reuse of reliable clinical code sets could accelerate the use of EHR data for research. To support that vision, there is an imperative need for methodologically. driven, transparent and automatic approaches to create error-free clinical code sets. Objectives: Propose and evaluate an automatic, generalizable, and knowledge-based approach that uses as starting point a correct and complete knowledge base of ingredients (e.g., the US Drug Enforcement Administration Controlled Substance repository list includes fentanyl as an opioid) to create medication code sets (e.g., Abstral is an opioid medication with fentanyl as ingredient). Methods: Algorithms were written to convert lists of ingredients into medication code sets, where all the medications are codified in the RxNorm terminology, are active medications and have at least one ingredient from the ingredient list. Generalizability and accuracy of the methods was demonstrated by applying them to the discovery of opioid and anti-depressant medications. Results: Errors (39 (1.73%) and 13 (6.28%)), obsolete drugs (172 (7.61%) and 0 (0%)) and missing medications (1,587 (41.26%) and 1,456 (87.55%)) were found in publicly available opioid and antidepressant medication code sets, respectively. Conclusion: The proposed knowledge-based algorithms to discover correct, complete, and up to date ingredient-based medication code sets proved to be accurate and reusable. The resulting algorithms and code sets have been made publicly available for others to use.
This thesis examines the re-entry processes of individuals with mental health needs upon their release from prison. In order to uncover the resources that are provided to formerly incarcerated individuals with clinically diagnosed mental health issues, parole officers who have experience supervising individuals with mental health needs were interviewed. The purpose of the interviews was to understand the experiences parole officers have regarding current supervision practices that are used, as well as programming and treatment opportunities parole officers know are available to this population of re-entrants. Being aware of the resources that are provided to formerly incarcerated individuals with mental health needs will help identify how to improve supervision, programming, and treatment so as to better support this population. As research and literature on the re-entry experiences of individuals with mental health care needs have demonstrated the extensive privations this population experiences, interviewing parole officers will reveal the roles parole officers, treatment providers, and programming have in supporting this population upon their release from prison. Moreover, interviewing parole officers will help identify how to improve parole outcomes for re-entrants with clinically diagnosed mental health issues.
Methods: This study was a randomized, wait-list, control trial with assessments at baseline and post-intervention (week 4). Participants were asked to meditate using Calm for 10 minutes per day. A p value ≤0.05 was considered statistically significant.
Results: The majority of participants (n=19) stated using Calm helped them cope with the stress of PA school. The intervention group participated in meditation for an average of 76 minutes/week. There were significant differences in all outcomes for the intervention group (all p ≤0.06). There was a significant interaction between group and time factors in emotional exhaustion (p=.016) and depersonalization (p=.025).
Conclusions: Calm is a feasible way to reduce burnout in PA students. Our findings provide information that can be applied to the design of future studies.