Erik W. Black

Ending the human immunodeficiency virus (HIV) epidemic relies on a robust clinical workforce. The Southeast AIDS Education and Training Center's interprofessional education program is a novel approach to increasing the interest and ability of early health professional learners to provide high-quality, comprehensive, person-first care for people with HIV. Key Points: Interprofessional education (IPE) focusing on multidisciplinary care for people with HIV can serve as a novel way to increase the HIV workforce. This brief report describes the IPE program of the Southeast AIDS Education and Training Center.

The flexibility afforded by online education may provide opportunities for learners with disability who require absence from traditional learning environments. This study sought to describe how a subset of learners with disability, those with hospital-homebound designation, perform in K-12 online classes, particularly as compared to non-hospital homebound counterparts. A cross-sectional analysis was performed of all Florida Virtual School course enrollments from August 1, 2012 to July 31, 2018. Researchers analyzed 2,534 course enrollments associated with K-12 students who, at the time of their course enrollment, had hospital-homebound designation, and a comparison group of 5,470,591 enrollments from K-12 students without hospital-homebound status. Data analysis showed three important outcomes. First, hospital-homebound designated student academic performance was equivalent to their non-hospital homebound counterparts. Second, however, hospital-homebound course enrollments were 26% more likely to result in a withdrawal prior to grade generation. Third, these withdrawals were potentially mitigated when H/H designated students were enrolled in five or more classes or in classes with five or more students. The results of this study provided evidence that when they can remain enrolled, hospital-homebound learners experience equivalent academic outcomes in online learning environments. These findings suggest that healthcare professionals should be made aware of the potentially equivalent outcomes for their patients. Moreover, virtual schools should seek to identify and create supports for these students.

BACKGROUND The United States public educational system encourages inclusion, integrating learners with different needs in the same classroom students, including those with chronic disability and illness. However, a small but significant number of students with chronic illnesses or disabilities may not be healthy enough to attend school in a traditional environment. Hospital homebound programs serve these children by providing educational instruction for those living with short-term and chronic disabilities in non-school settings. These programs are publicly supported, differing significantly from homeschooling where, traditionally, a child’s parent or guardian assumes responsibility for the delivery of educational services. The limited research exploring hospital homebound programs describes them as challenged, characterized by instructors who may lack the qualifications to teach critical core subject matters and teach with limited instructional time. As online learning continues to become more mainstream in the United States, it is important to explore the impact that the medium could have on students with differing needs. The flexibility afforded by online education may provide opportunities for learners with disability that necessitates absence from traditional learning environments. OBJECTIVE This study sought to describe how a subset of learners with disability, those with a hospital-homebound designation, perform in K-12 online classes, particularly as compared to non-hospital homebound counterparts. METHODS A cross-sectional analysis was performed of all Florida Virtual School course enrollments from August 1, 2012, to July 31, 2018. Researchers analyzed 2,534-course enrollments associated with students who, at the time of their course enrollment, had hospital-homebound designation, and a comparison group of 5,470,591 enrollments from students without hospital-homebound status. RESULTS Hospital-homebound designed student academic performance was equivalent to their non-hospital homebound counterparts (P = .05 - .28). But, hospital-homebound course enrollments were 26% more likely to result in a withdrawal prior to grade generation (P < .01). CONCLUSIONS Hospital-homebound students represent a population under-served by many education systems, including online education. The results of this study provided evidence that when they can remain enrolled, hospital-homebound learners experience equivalent academic outcomes in online learning environments. These findings suggest that healthcare professionals should be made aware of the potentially equivalent outcomes for their patients, and virtual schools should seek to identify and create supports for these students.

Problem: There is an unmet need for economically feasible, valid, reliable, and contextually relevant assessments of interprofessional collaborative knowledge and skills, particularly at the early stages of health professions education. This study sought to develop and gather content and internal structure validity for an Interprofessional Situational Judgement Test (IPSJT), a tool for the measurement of students’ interprofessional collaborative intentions during the early stages of their professional development. Approach: After engaging in an item development and refinement process (January–June 2018), an 18-question IPSJT was administered to 953 first-year students enrolled in 10 health professions degree programs at the University of Florida Health Science Center in October 2018. The IPSJT’s performance was evaluated using item-level analyses, item difficulty, test-retest reliability, and exploratory factor analysis. Outcomes: Seven hundred thirty-seven (77.3%) students consented to the use of their data. Student IPSJT scores ranged from 0 to 69, averaging 42.68 (standard deviation = 12.28), with some statistically significant differences in student performance by health professions degree program. IPSJT item difficulties ranged from 0.13 to 0.92. Once one item with poor properties was excluded from analysis, the IPSJT demonstrated an overall reliability of 0.62. Students were more successful at identifying the least effective than the most effective responses. Test-retest reliability provided evidence of consistency ( r = 0.50, P < .001) and similar item difficulty across administrations. An exploratory factor analysis indicated a 3-factor model with multiple cross-factor loadings. Next Steps: This work represents the first step toward the development of a valid, reliable IPSJT for early learners. The emergent 3-factor model provides evidence that multiple competencies can be assessed in early learners via this tool. Additional research is necessary to build a more robust question bank, explore different scoring and response methods, and gather additional sources of validity evidence, including relations to other variables.

Purpose: Despite the importance of early intervention and remediation, the relatively short duration of physician assistant education programs necessitates the importance of early identification of at-risk learners. This study sought to ascertain whether machine learning was more effective than logistic regression in predicting remediation status among students, using the limited set of data available before or immediately following the first semester of study as predictor variables and academic remediation as an outcome variable. Methods: The analysis included one institution and student data from 177 graduates between 2017 and 2019. We employed one data mining model, random forest trees, and compared it to a traditional predictive analysis method, logistic regression. Due to the small sample size, we employed leave-one-out cross-validation and bootstrap aggregation. Results: Data provided evidence that the random forest algorithm correctly identified individuals who would later experience academic intervention with a 63.3% positive predictive value, whereas logistic regression exhibited a positive predictive value of 16.6%. Conclusions: This single-institution study indicates that predictive modeling, employing machine learning, may be a more effective means than traditional statistical methods of identifying and providing assistance to learners who may experience academic challenges.