Jessica Y Hsueh, Daniel Nethala, Shiva Singh, W Marston Linehan, Mark W Ball
Investigating the clinical reasoning abilities of large language model GPT-4: an analysis of postoperative complications from renal surgeries
Urologic oncology
advance understanding doctoral student experiences high attrition rates among science technology engineering mathematics stem doctoral students developed examined psychological profiles different types doctoral students used latent class analysis selfreported psychological data relevant psychological threat 1081 incoming doctoral students across three universities found bestfitting model delineated four threat classes lowest threat nonchalant engagedworried highest threat classes associated characteristics measured beginning students first semester graduate school may influence attrition risk including differences academic preparation eg amount research experience selfevaluations perceived fit eg sense belonging attitudes towards graduate school academia eg strength motivation interpersonal relations eg perceived social support lowest threat students tended report positive characteristics highest threat students negative characteristics whereas results nonchalant engagedworried students mixed ultimately suggest engagedworried highest threat students relatively high risk attrition moreover demographic distributions profiles differed members groups likely face social identity threat eg women overrepresented higher threat profile ie engagedworried students underrepresented lower threat profiles ie lowest threat nonchalant students conclude doctoral students meaningfully vary psychological threat beginning graduate study suggest differences may portend divergent outcomes