Academic Psychiatry最新文献

Objective: This study investigates the impact of the psychiatry rotation in developing students' clinical skills that are applicable across various medical specialties.

Methods: Medical students at Saint Louis University completed the same survey before and after completing a 3½-week psychiatry rotation. Students rated 18 statements on a Likert scale. Statements were grouped into six categories to assess students' confidence in (1) managing medical issues of psychiatric patients, (2) addressing injurious behaviors, (3) gathering a clinical history, (4) de-escalating crises, (5) self-regulating emotions, and (6) empathizing with patients. Students' pre- and post-survey numerical responses were added within the six categories and compared using paired t-tests.

Results: Forty-three students completed both the pre-clerkship and post-clerkship surveys. For all six clinical skills categories, post-survey composite scores significantly increased. Managing medical complaints had a 7-point median score increase. Addressing injurious behavior had a 3-point median increase. Gathering a clinical history had a 4-point median increase. De-escalating crises had a 4-point median increase. Emotional self-regulation had a 2-point median increase. Empathizing with patients had a 2-point median increase. All six paired t-tests between pre- and post-surveys showed p-values < 0.001, indicating subjective improvement in students' performance for each clinical competency.

Conclusion: The psychiatry rotation effectively increased medical students' perceived acquisition of essential clinical skills. These competencies, including gathering a history, emotionally self-regulating, and de-escalating crises, apply to nearly all medical specialties. Further research is needed to investigate which specific opportunities helped students develop these skills and the broader strengths of the psychiatry clerkship.

Objective: This study was designed to investigate if artificial intelligence (AI) detection software can determine the use of AI in personal statements for residency applications.

Method: Previously written personal statements were collected from physicians who had already matched to residency through the Electronic Residency Application System. Physicians were recruited for the study through collegial relationships and were given study information via email. The study team constructed five parallel statements from the shared personal statements to prompt AI to create a personal statement of similar content. An online AI detection tool, GPTZero, was used to assess all the personal statements. Statistical analyses were conducted using R. Descriptive statistics, t-tests, and Pearson correlations were used to assess the data.

Results: Eight physicians' statements were compared to eight AI-generated statements. GPTZero was able to correctly identify AI-generated writing, assigning them significantly higher AI probability scores compared to human-authored essays. Human-generated statements were considered more readable, used shorter words with fewer syllables, and had more sentences compared to AI-generated essays. Longer average sentence length, low readability scores, and high SAT word percentages were strongly associated with AI-generated essays.

Conclusions: This study shows the capacity of GPTZero to distinguish human-created versus AI-generated writing. Use of AI can pose significant ethical challenges and carries a risk of inadvertent harm to certain applicants and erosion of trust in the application process. Authors suggest standardization of protocol regarding the use of AI prior to its integration in post-graduate medical education.