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Abstract Background Over 80% of people who experience a stroke present with residual impairment of the upper extremity, such as the shoulder, elbow, wrist, and hand. However, rarely do stroke rehabilitation practitioners (e.g., occupational therapists) use standardized outcome measures to objectively evaluate upper extremity function. Accordingly, the purpose of this project was to develop a bundle of implementation strategies that supports practitioners’ adoption of the Fugl-Meyer Assessment of the Upper Extremity in stroke rehabilitation practice. Methods We used tenets of Implementation Mapping to guide the development of our implementation strategy bundle. We partnered with one, large academic health system serving over 200 stroke patients annually through intensive rehabilitation care. Strategies were selected and developed through a multi-method process that included a review of the literature, qualitative input from our health system’s practitioners and managers, and expert consultation. We also specified the hypothesized implementation “mechanisms” our strategies intended to change. Practitioners’ adoption (yes/no) of the Fugl-Meyer Assessment was calculated by analyzing electronic health record documentation of the 6-month time frame before strategies were deployed compared to the 6-month time frame after deployment. Results Practitioners were exposed to the following implementation strategies to support Fugl-Meyer adoption: conduct educational meetings, prepare outcome measure champions, provide equipment, develop training materials, and adapt documentation systems. In the 6-months before deployment of our implementation strategies, practitioners implemented the Fugl-Meyer with 14.8% of stroke patients. In the six months after deployment, adoption of the Fugl-Meyer increased to 73.8% (p

Graduate admissions committees throughout the United States examine both quantitative and qualitative data from applicants to make admissions determinations. A number of recent studies have examined the ability of commonly used quantitative metrics such as the GRE and undergraduate GPA to predict the likelihood of applicant success in graduate programs. We examined whether an admissions committee could predict applicant success at The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences based on quantitative metrics. We analyzed the predictive validity of admissions scores, undergraduate GPA, and the GRE for student success. We observed nuanced differences based on gender, ethnicity, race, and citizenship status. The scores assigned to applicants by the admissions committee could not predict time to degree in PhD students regardless of demographic group. Undergraduate GPA was correlated with time to degree in some instances. Interestingly, while GRE scores could predict time to degree, GRE percentile scores could predict both time to degree and PhD candidacy examination results. These findings suggest that there is a level of nuance that is required for interpretation of these quantitative metrics by admissions committees.