Josephine Langton, Sara Liaghati-Mobarhan, Edith Gicheha, Jennifer Werdenberg-Hall, June Madete, George Banda, Elizabeth M Molyneux
Using interprofessional education to build dynamic teams to help drive collaborative, coordinated and effective newborn care
BMC pediatrics
diagnostic causal reasoning goal infer probability causes one multiple observed effects typically studies investigating tasks provide subjects precise quantitative information regarding strength relations causes effects sample data relevant quantities learned contrast sought examine peoples inferences causal information communicated qualitative rather vague verbal expressions eg x occasionally causes conducted three experiments using sequential diagnostic inference task multiple pieces evidence obtained one quantitative predictions different probabilistic models derived using numerical equivalents verbal terms taken unrelated study different subjects present novel bayesian model allows incorporating temporal weighting information sequential diagnostic reasoning used model primacy recency effects basis 19848 judgments 292 subjects found remarkably close correspondence diagnostic inferences made subjects received verbal information matched control group information presented numerically whether information conveyed verbal terms numerical estimates diagnostic judgments closely resembled posterior probabilities entailed causes prior probabilities effects likelihoods observed interindividual differences regarding temporal weighting evidence sequential diagnostic reasoning work provides pathways investigating judgment decision making verbal information within computational modeling framework