Scholars of intersectional identities face unique challenges in their departments including disproportionate workloads, excessive service assignments, and additional mentorship obligations. Patricia A. Matthew, an English department faculty member at Montclair State University, describes these extra commitments as “invisible labor” - because institutions do not value this labor with the currency they typically use to reward faculty work: reappointment, tenure, and promotion. This webinar session, hosted by SSRC PROGRESS, is structured around a participant-led discussion of two case studies, with two discussion facilitators to support a meaningful dialogue on issues of department climate and academic workload/career advancement. The ultimate goal of this session is to have an open discussion of both challenges and successes in employing DEI interventions in anthropology departments.

PROGRESS is an NSF ADVANCE adaptation grant program that addresses organizational policies and procedures related to advisory board formation, peer review, and fellowship/awards conferment to ensure that diversity, equity, and inclusion measures are meaningful and effective. Housed within the Social Science Research Council, PROGRESS partners with five disciplinary associations - the American Anthropological Association, the American Economic Association, the American Historical Association, the American Political Science Association, and the American Sociological Association.

Presenters

* Veronica Zepeda, Program Director, Sloan Scholars Mentoring Network - Social Science Research Council
* Fred Palm, VP of Administration and Operations, P.I. of PROGRESS - Social Science Research Council

Department Climate and Academic Workload in Anthropology Departments

Medical students have reported higher rates of stress and depression compared to their age-matched peers, which worsen while studying in isolation for high stakes examinations such as the United States Medical Licensing Examination (USMLE) Step 1. Medical schools should therefore implement and evaluate additional support offerings to improve the student experience during this time. We implemented a support framework, titled Step-Up, to directly address isolation for students studying for Step 1 in the winter of 2021-2022. This included multiple group, individual, and email interventions. Students were asked to self-report wellness and connectivity before and during Step 1 studying, levels of satisfaction and usefulness of the resources offered. As expected, self-reports of wellness and connectivity worsened during Step 1 study. Many interventions had low uptake. Students reported highest satisfaction with and usefulness of weekly emails and near-peer tutors. Our data show that near-peer tutoring and regular support emails are successful interventions to help mitigate the worsening wellness and increased isolation medical students experience during Step 1 studying. It also examines multiple other interventions that educators may wish to study further, or not include due to lack of uptake or benefit.

Step-Up to Address Step 1 Anxiety and Improve Wellness

COVID-19 Vaccine Uptake and Perceptions among Hispanics/Latinos: Disparities, Hesitancy, and Implications for Equitable Access

**Scoring is only possible in the semifinals hall**

**Abstract:**



Spectrum: An Early Exposure Program

Background: Predicting the discharge destination, otherwise known as discharge disposition, can offer several advantages for the inpatient rehabilitation patient, including treatment and discharge planning. Machine learning (ML) models have demonstrated a strong predictive capacity for discharge disposition, particularly for patients with specific diagnoses such as traumatic brain injuries, or in specific settings such as acute neurologic care, or among specific populations. 



Objective: Explore various ML models’ ability to predict inpatient rehabilitation discharge disposition to home versus non-home using patient encounter data. 



Methods: Patient data included demographics, diagnoses, lab values, length of stay, and insurance status from all inpatient rehabilitation encounters at a large midwestern health system occurring between 2015 and 2022 (N=4919). We compared two commonly used ML approaches: a random forest (RF) classifier and a decision tree (DT) classifier. Two bagging classifiers, which aggregate the results of multiple RF or DT classifiers to improve predictions, were also tested. Because the imbalance in number between home (N=3687) and non-home discharges (N=1232) can impair predictions, balancing techniques were used prior to training the ML classifiers. The ML classifiers were then optimized with grid search and cross validation. Performance of the ML classifiers was determined by the receiver operating characteristic area under the curve (AUC) – a measure of the ability to discriminate the true discharge disposition, accuracy (computed using the Jaccard Index), and macro f1-score (indicative of classification ability across classes). 



Results: 

A bagging classifier using RF showed highest predictive performance in terms of AUC (0.68) and accuracy (0.75), however it struggled to predict non-home discharges (macro f1-score = 0.54). A bagging DT classifier improved prediction of non-home discharges (macro f1-score = 0.59), however AUC (0.65) and accuracy (0.69) were lower. Generally, bagging classifiers combining the predictions from multiple RF or DT classifiers improved predictive performance compared to single RF or DT classifiers. 



Conclusions: As artificial intelligence and ML continue to grow in healthcare applications, this study showcased the effectiveness of ML approaches to predict discharge disposition from inpatient rehabilitation. Our findings complement previous studies in other diagnoses and settings, due to our diversity of available variables and differences in our patient population and treatment paradigms. Of note, utilization of bagging classifiers proved valuable in enhancing the performance of the tested base classifiers. Our results may help guide optimization of ML approaches to predict discharge disposition, in hopes of improving patients' hospital courses and post-hospital planning and treatment.

Evaluation of Machine Learning Approaches for Predicting Inpatient Rehabilitation Patient Discharge Disposition

Introduction
Clostridium difficile (C. diff) is the leading cause of nosocomial diarrhea and has been shown to impact about 13 in every 1000 hospitalized patients. The occurrence of C. diff and Inflammatory bowel disease (IBD) have been linked to disruption of the gut microbiota (dysbiosis) which may explain the parallel rise of C. diff infection in IBD patients. Our study aimed to analyze the cost burden and clinical outcomes of hospitalized IBD patients with C. diff infections.
Methods
240,550 adults hospitalized with Crohn’s disease or Ulcerative colitis were analyzed using the NIS data from 2016-2019. Patients were classified into a group with C. diff and a group without C. diff and variables like Age, gender, race, diabetes mellitus, nicotine use, anemia, end-stage renal disease (ESRD), thrombocytopenia, albumin abnormality, peritoneal abscess, toxic megacolon, gastrointestinal bleed, vesicovaginal fistula, anorectal fistula, vesicointestinal fistula, mortality, and hospitalization cost were analyzed using the two-sample t test, chi-square test, and multiple linear regression. P value < 0.05 was considered statistically significant.
Results
We found that compared to men, women hospitalized with IBD are more likely to get infected with C. diff (4.2% vs 4.5%, P = < 0.001). Older IBD patients (Mean age of 53.1) are at risk compared to other age groups (P = < 0.001). Whites had the least C. diff rates compared to blacks, Hispanics, Asians, Native America, and others (4.2% vs 4.7% vs 5.6% vs 5.3% vs 4.4% vs 4.5%, P = < 0.001). IBD patients with anemia and thrombocytopenia had a significantly higher rate of C. diff infection (P = < 0.001). Interestingly, nicotine dependence and peritoneal abscess protected against C. diff infection (P = < 0.001 and 0.008). The average total hospital cost among IBD patients with C. diff was $20,254 higher compared to those without C. diff after controlling the effects of the variables - age, gender, race, nicotine status, anemia, ESRD, peritoneal abscess, thrombocytopenia, gastrointestinal bleed, and vesicovaginal fistula. The odds of mortality among IBD patients with C. diff was more than twice compared to those without C. diff (OR = 2.1, p = < 0.001).
Discussion
IBD patients remain at increased risk of C. diff infection and more hospital expenses. Hence, clinicians should rule out C. diff infection first in every IBD patient presenting with disease flare-ups or diarrhea.


In-hospital Mortality, Outcomes and Cost Analysis of Hospitalized Inflammatory Bowel Disease Patients with Clostridium difficile Infection.


Background
Research is recommended in medical training and residency, as per the Accreditation Council for Graduate Medical Education (ACGME). This not only encourages academic interests among residents but also helps them adopt evidence-based approaches while treating patients. Early research training has been linked to continued scholarly work and informed career choices. However, many medical students and residents focus more on non-hypothesis-driven research, likely due to inadequate technical support and low interest. We have developed an OpenAI GPT3-based web application called AutoAnalyze to address this issue. 
Methods
The program was created using Visual Studio Code, and the code was written with Python, along with Streamlit and a variety of visualization packages. For ad hoc visualization, additional programs are invoked de novo by user input. Application programming interfaces (APIs) for OpenAI ChatGPT3.5 were integrated for activation depending on specific user feature choices. Custom prompts and APIs enabled highly focused functionality, with coding details available on 
GitHub repository under the MIT licence: https://github.com/DrDavidL/auto_analyze.Streamlit.io hosts the application on a public site: https://data-auto-analysis.streamlit.app/.
Results
AutoAnalyze simplifies the technical aspects of hypothesis-driven research, making it easy to conduct research using pre-coded options or via natural language inputs. With AutoAnalyze, users can perform data exploration, missing data imputation, and data visualization. Additionally, it generates publication-worthy images like bar charts, pie charts, scatterplots, and principal component analysis with just a few clicks. AutoAnalyze also provides teaching pearls on visualization techniques and summarizes relevant statistics in table format. AutoAnalyze can perform machine learning analytic methods like logistic regression, decision trees, random 
forests, gradient boosting machines (GBMs), and support vector machines (SVM) to perform predictive analysis in an easy-to-comprehend user-friendly format. It does this without requiring the user to type in any code or understand a computer programming language. Furthermore, the chatbot teacher feature enables users to probe data or learn data science using regular English words and sentences.
Conclusion
Overall, AutoAnalyze represents an efficient solution to address the inadequate technical support for medical research challenges medical students and residents face. It has the potential to increase their research interest in academic medicine.


Increasing Interest in Medical Research and Academic Medicine With Auto Analyzer; an OpenAI GPT-3-Based Web Application

Background: The COVID-19 pandemic exposed the gap in the effective communication of health information between the medical community and general public. For example, vaccine hesitancy demonstrated the need to engage patients in challenging conversations, explain complex scientific research and advancements, and appropriately acknowledge areas of medical uncertainty. To meet this need, medical educators play an important role in the training of rising physicians. Specifically, we can employ innovative training models such as medical improv which utilizes improvisational theatre principles to enhance trainees’ communication skills. To this end, we examined the use of medical improv as a tool for teaching clinical communication skills to UC Davis medical students ahead of their first clinical clerkships.
Methods: We piloted a four-part medical improv workshop for second-year medical students in January of 2022 to teach core improv communication skills such as active listening, quick thinking, and collaboration. After initially assessing the workshop impact through pre-and-post workshop surveys, we conducted one year of follow up by distributing digital surveys after 4 of the 7 clinical clerkships. Surveys were comprised of Likert scale and open-response questions to assess how, if at all, medical students employed improv communication skills during clerkships. Data was analyzed using mixed methods. Of note, enrollment was limited due to the spread of the Omicron variant in Winter of 2022.
Results: Of the 10 enrolled students, 8 (80%) completed the workshop, and 3-7 respondents submitted each follow-up survey. Participants implemented improv skills an average of 3 times during a day of clinic activity. Thematic analysis of the narrative responses revealed that students most commonly implemented their improv skills when 1) actively listening to patients, 2) answering questions posed by patient’s families, and 3) having difficult conversations. Students described how active listening was critical to establishing connections with patients and understanding their perspectives. One student remarked, “actively listening to the patient was much more important than trying to pre-form a response to what I imagine they are going to say.”
Conclusion: After one-year of follow up, this pilot study shows that medical improv can meaningfully impact students’ clinical communication skills by teaching the difference between responding and reacting in conversations with patients and their families. Through honing active listening skills, we can better equip future physicians to minimize the communication gap between them and their patients.

Responding vs. Reacting: How medical improv can enhance clinical communication skills in rising physicians

This study evaluated the Collaborative Approach to Learning Medicine (CALM) program at Rutgers New Jersey Medical School, a peer-to-peer educational support initiative for first-year medical students. A pre-post study was conducted in the 2022-2023 academic year, analyzing survey responses from 47 participants. Pre-survey results indicated students' perceptions of the program, with question banks being the most effective resource (mean score: 4.2) and student-led tutoring as the least effective (mean score: 3.5). After participation, the effectiveness of overview sessions, review sessions, and anatomy course videos significantly increased (p<.05). Qualitative feedback emphasized improving question quantity and quality. This study highlights the CALM program's effectiveness in peer-to-peer teaching while recommending resource allocation to enhance frequently used components.

Perceptions of Medical Students Towards the Effectiveness of the Collaborative Approach to Learning Medicine Program

Introduction:
High-fidelity three-dimensional (3D) models provide a better understanding of spatial relationships among complex anatomical structures. Medical students often lack hands-on opportunities to develop an in-depth understanding of applied anatomy prior to their clinical years, a trend exacerbated by the COVID-19 pandemic. There is limited research on the utilization of 3D models in the clinical years. This study evaluates the effectiveness of an interactive 3D-printed inguinal hernia model in teaching applied surgical anatomy to third-year medical students (MS3).

Materials and Methods:
3D-printed models of inguinal anatomy were created using de-identified CT scans, 3D-modeling software, and 3D-printing services. A single-blind randomized control trial of third-year medical students was conducted at a single academic institution. The tactile-group received three educational tools describing inguinal hernia anatomy and repair: an established video module (WISE-MD), an illustrated procedure guide, and a 3D-printed model. The non-tactile group was provided the WISE-MD module and the illustrated procedure guide. Both groups received pre- and post-tests on basic and applied anatomy. Statistical analysis was performed using a t-test or Mann–Whitney U test for continuous variables and Chi-square or Fisher's exact tests for categorical variables. Statistical significance was defined by p <0.05.

Results:
One hundred and three third-year medical students participated in this study. There were no differences in pre-test scores between study groups (p= 0.98). The tactile-group scored better on post-test questions on applied anatomy (p= 0.03), but no differences were noted in scores on basic anatomy questions (p= 0.41). When comparing performance between overall pre-test and post-test scores, the tactile group demonstrated improvement in test scores which trended toward significance (p= 0.09).

Conclusions:
Third-year medical students using high-fidelity 3D-printed models demonstrated improved test scores on applied anatomy when compared to students who were exposed to conventional educational resources. With the rising use of virtual learning environments, interactive learning tools like 3D-printed anatomy models may have an important role in knowledge acquisition during the surgical clerkship.

3D Printing in Surgical Education:A Visuospatial and Kinesthetic Learning Module of Inguinal Hernia Repair for Third-Year Medical Students

Background
As physician shortages increase in the United States, many medical schools across the country have established regional medical campuses (RMCs) to increase class size. The University of Missouri-Columbia (MUSOM) created an RMC, the Springfield Clinical Campus (SCC), in 2016. All students spend the preclinical years at the central campus in Columbia, then either continue in Columbia or move to Springfield for the clinical years. 
Some research has been published on the correlation between RMC training and in-state postgraduate training. However, no publications have discussed the relationship between clinical RMC training and in-region postgraduate training. Geographic regions have become important for residency applicants in recent years as regional preferencing has been integrated into the ERAS application. For the 2022-2023 ERAS cycle, all specialties participated in geographic preferencing except for obstetrics and gynecology (OBGYN) and emergency medicine. This has changed for the upcoming 2023-2024 application cycle, in which OBGYN applicants are now encouraged to specify up to three preferred geographic regions. We collected data on whether central versus regional clinical campus was correlated to postgraduate training in an outside region prior to the implementation of geographic preferencing using OBGYN match results of graduates of MUSOM.

Methods
We utilized publicly available OBGYN match data from 2018-2023 for MUSOM. Residency programs were labeled by geographic region according to the regional designations utilized by the Electronic Residency Application Service (ERAS). A Fisher exact test was performed to calculate the two-tailed p-value comparing the proportion of students at each campus who matched in region versus out of region.

Results
In total, 34 students matched into OBGYN from MUSOM and elected to make this data public. Of these, 8 students attended the SCC and 26 students attended the central campus. At the SCC, 25% of students matched in region, while 75% matched out of region. At the central campus, 38% of students matched in region, while 62% matched out of region. There was no statistical difference between the campuses for the proportion of students matching in region versus out of region (p=0.6809). 

Conclusion
No difference was found between the RMC and central campus for the proportion of students matching in region versus out of region for OBGYN residency. Subsequent research following implementation of geographic signaling would likely be useful for elucidating trends among the campuses. Future studies may also investigate this correlation in medical schools with RMCs in which both preclinical and clinical years are completed at the RMC.


Geographic Locations of Obstetrics and Gynecology Residency Programs Matched by Regional and Central Campus Graduates

Background
Type 2 diabetes mellitus (T2DM) is a global epidemic affecting approximately 9.3% of the world population and has an all-cause mortality rate of 8.5%. An increasing number of patients with chronic conditions access online resources for educational purposes, with recent statistics suggesting that up to 80% of adults in the United States have obtained medical information through online search engines. This study aims to assess the accuracy of the information offered by artificial intelligence (AI) chatbots to T2DM questions using ChatGPT.

Methods:
An initial set of questions about T2DM and its complications were obtained from an online platform such as American Diabetes Association. Subsequently, these questions underwent a thorough evaluation by physicians well-versed in diabetes management before being presented to ChatGPT. Each question was introduced to ChatGPT three times, and the responses were recorded. Two study staff reviewed the three sets of responses and summarized the majority response that was repeated at least twice. Data were reviewed by two board-certified internal medicine physicians with a minimum of 11 years of experience. Responses were categorized into appropriate; provide accurate information that meets the standard of care, inappropriate; contain erroneous information but meets the minimal standard of care, and unreliable; the response provides inconsistent content that does not meet the standard of care.


Results:
Of the original 110 questions, 70 were included in the final dataset. Out of the 70 summarized responses generated by ChatGPT, 89.3% were categorized as appropriate, indicating that the AI model provided accurate information meeting the standard of care for Type 2 diabetes management and its associated complications. On the other hand, 10% of the responses were deemed inappropriate, containing errors but still meeting the minimal standard of care in addressing the questions. The remaining 0.7% of ChatGPT's responses were classified as unreliable, displaying inconsistent content that did not meet the standard of care in terms of accuracy and reliability


Conclusion
ChatGPT offers a promising source of information for educating patients with T2DM. The study demonstrates high accuracy in providing appropriate responses that align with the standard of care, positioning it as a useful tool in enhancing patient education and diabetes management.


Underline digital video library

Department Climate and Academic Workload in Anthropology Departments

Step-Up to Address Step 1 Anxiety and Improve Wellness

COVID-19 Vaccine Uptake and Perceptions among Hispanics/Latinos: Disparities, Hesitancy, and Implications for Equitable Access

Spectrum: An Early Exposure Program

Evaluation of Machine Learning Approaches for Predicting Inpatient Rehabilitation Patient Discharge Disposition

In-hospital Mortality, Outcomes and Cost Analysis of Hospitalized Inflammatory Bowel Disease Patients with Clostridium difficile Infection.

Increasing Interest in Medical Research and Academic Medicine With Auto Analyzer; an OpenAI GPT-3-Based Web Application

Responding vs. Reacting: How medical improv can enhance clinical communication skills in rising physicians

Perceptions of Medical Students Towards the Effectiveness of the Collaborative Approach to Learning Medicine Program

3D Printing in Surgical Education:A Visuospatial and Kinesthetic Learning Module of Inguinal Hernia Repair for Third-Year Medical Students

Geographic Locations of Obstetrics and Gynecology Residency Programs Matched by Regional and Central Campus Graduates

Artificial Intelligence Shifting the Paradigm of Diabetes Care

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