Journal of graduate medical education最新文献

Background Social isolation and loneliness (SIL) are strongly associated with poor health outcomes among older adults; however, graduate medical education often limits dedicated geriatrics clinical training, leaving residents ill-prepared to care for older adults. Engaging residents with older adults in nonclinical environments could change perceptions on SIL and aging and improve care for older adults. Objective To develop and evaluate a social constructivism-based curriculum for interns centered on nonclinical engagement with older adults, aimed at enhancing empathy and understanding of SIL. Methods Twenty-nine internal medicine interns at an academic medical center in Oregon participated in mandatory educational sessions on SIL and completed structured outreach calls to older adults at elevated risk of SIL from 2020 to 2021. On completion of these activities, interns' self-reflection essays about their experiences were analyzed to identify key themes and changes in their perceptions and attitudes. Results Analysis of interns' self-reflection essays yielded 7 major themes, including changes in their understanding of SIL and attitudes toward older adults, often prompted by older adults' resilience and resourceful coping strategies. Other major themes included lessons learned about SIL, factors affecting SIL, and intended changes to future practice. Conclusions By creating opportunities for interns to reflect on biases and engage with the realities of older adults' lives, our curriculum led to a shift in interns' perceptions toward a more thoughtful understanding of aging and SIL and intentions to integrate a more complex understanding of aging and SIL into their future work.

Background Geographic maldistribution of the physician workforce persists, despite substantial investment in expanding graduate medical education (GME). Training programs remain concentrated in urban centers, and physicians often practice near where they train, reinforcing regional imbalances in workforce distribution. Understanding trainee mobility patterns is essential for identifying how uneven resource distribution perpetuates workforce shortages in underserved areas and developing targeted interventions to improve geographic disparities in physician supply. Objective To develop and apply a scalable framework for evaluating regional retention and mobility patterns of physician trainees across 3 sequential stages of GME using ophthalmology as an exemplar specialty. Methods This is a retrospective cohort study of 800 ophthalmology residency graduates between 2019 and 2022 from 52 Accreditation Council for Graduate Medical Education accredited programs. Using publicly available data from the FREIDA Residency Program Database and residency program websites, geographic transitions were tracked between medical school and residency (T1), residency and fellowship (T2a), and residency and first attending position (T2b). Primary outcomes were regional retention and travel distance in miles between medical school or residency-affiliated hospital addresses. Results The South had the highest retention during T1, while the West retained the most graduates during T2a and T2b. The West showed the lowest retention during T1, and the Midwest during T2a. Trainees traveled farthest during T2a compared to T1 and T2b (P<.005). Conclusions Trainee relocation and retention patterns varied by training stage and region, with the greatest mean travel distance occurring between residency and fellowship.

Background The improvement of generative artificial intelligence (AI) has led to concerns about residency applicants using AI to write personal statements. Because some program directors may value fully human-generated personal statements, they may be inclined to use commercially available AI detection tools. However, the accuracy of AI detection in personal statements is uncertain. Objective To evaluate the accuracy of AI detection tools in identifying AI-generated content within residency personal statements. Methods In 2024, 25 human-generated personal statements were collected from residents in the fields of internal medicine, psychiatry, neurology, and surgery at a single institution. The authors made 25 AI-generated statements with ChatGPT-4o, and 25 personal statements that were a combination of AI-generated and human-generated content (mixed content). Four AI detection tools (including free and paid tools) were used to compare the likelihood each statement was AI-generated. Summary statistics and multivariate analysis of variance (MANOVA) with post hoc Tukey test were performed. Results AI detection tools varied in the likelihood scores that were assigned to human-generated personal statements (mean likelihood of statement being AI generated, min-max range of likelihoods): non-disclosed paid detector (9.7%, min-max: 0-84%), Writer (1.6%, min-max: 0-9%), GPTzero (4.5%, min-max: 3-22%), and ZeroGPT (17.2%, min-max: 0-70.5%). MANOVA and post hoc tests revealed significant differences in likelihoods between the groups (P<.001). However, there was overlap between mixed content and completely AI-generated personal statements. Conclusions The detection tools occasionally assigned high AI likelihood scores to human-generated content and were unable to reliably distinguish mixed-content texts from AI-generated texts.