No abstract

BackgroundRecently, there has been increasing focus on skills that are crucial for success in residency that is not explicitly taught. Specifically, the four domains of teaching skills, evidence appraisal, wellness, and education on structural racism have been identified as topics that are important and underrepresented in current resident education curriculums, largely due to time constraints. MethodsA task force consisting of one post-graduate year 2 (PGY-2) resident, one PGY-4 resident, the Associate Program Director, and the Program Director of the Internal Medicine-Pediatrics residency program was formed to explore current deficiencies in resident curriculum and to research possible solutions. As an intervention, we created and executed a four-week academic elective with dedicated time for upper-level residents to learn and explore the four domains of resident teaching, evidence-based clinical practice, wellness, and anti-racism work. The elective included several clinical sessions dedicated to implementing the skills taught in the elective. The month-long elective completed in January 2021. All residents evaluated each lecture or experience based on how valuable it was to their education on a Likert scale from 1 to 7, with 1 defined as "not valuable at all" and 7 defined as "extremely valuable." ResultsResidents rated the overall value of teaching in each domain highly. Education and activities in wellness lectures were found to have the highest value-added material (6.20 ± 0.41, n = 18), followed by residents-asteachers lectures (5.93 ± 0.25, n = 48), anti-racism (5.57 ± 1.11, n = 9), and evidence-based clinical practice (5.18 ± 0.50, n = 43). In addition, each domain was found to have at least one high-yield topic. ConclusionsWe were able to create and execute an academic elective with dedicated time for upper-level residents to develop and utilize valuable skills in teaching, evidence appraisal, wellness, and anti-racism. Future work will focus on refining the curriculum based on resident evaluations and expanding this elective to the Internal Medicine and Pediatrics categorical programs at our institution.

Problem: Although holistic review has been used successfully in some residency programs to decrease bias, such review is time-consuming and unsustainable for many programs without initial prescreening. The unstructured qualitative data in residency applications, including notable experiences, letters of recommendation, personal statement, and medical student performance evaluations, require extensive time, resources, and metrics to evaluate; therefore, previous applicant screening relied heavily on quantitative metrics, which can be socioeconomically and racially biased. Approach: Using residency applications to the University of Utah internal medicine–pediatrics program from 2015 to 2019, the authors extracted relevant snippets of text from the narrative sections of applications. Expert reviewers annotated these snippets into specific values (academic strength; intellectual curiosity; compassion; communication; work ethic; teamwork; leadership; self-awareness; diversity, equity, and inclusion; professionalism; and adaptability) previously identified as associated with resident success. The authors prospectively applied a machine learning model (MLM) to snippets from applications from 2023, and output was compared with a manual holistic review performed without knowledge of MLM results. Outcomes: Overall, the MLM had a sensitivity of 0.64, specificity of 0.97, positive predictive value of 0.62, negative predictive value of 0.97, and F1 score of 0.63. The mean (SD) total number of annotations per application was significantly correlated with invited for interview status (invited: 208.6 [59.1]; not invited: 145.2 [57.2]; p < .001). In addition, 8 of the 10 individual values were significantly predictive of an applicant’s invited for interview status. Next Steps: The authors created an MLM that can identify several values important for resident success in internal medicine–pediatrics programs with moderate sensitivity and high specificity. The authors will continue to refine the MLM by increasing the number of annotations, exploring parameter tuning and feature engineering options, and identifying which application sections have the highest correlation with invited for interview status.

No abstract

It's not a smoke, it's a ritual to my ancestors. his face smolders, charred from his last Pall Mall prayer, when his cannula wafted sweet incense to sacrificial lips, igniting cathedral lungs: Intubated despite DNI. Tobacco is my first amendment right. I've sued and won before! his words a rosary of exhausted pleas without penance.