Impostor Syndrome is the belief or feeling that one is passing oneself off as much more capable than one really is. Anecdotally, it is experienced more by members of historically disadvantaged groups, but the empirical data seems inconsistent with this view. I argue that impostor syndrome occurs because (a) it is normal, appropriate and often even necessary to engage in some degree of pretense in order to acquire specialist expertise, but (b) we are much more likely to be aware of (...) our own pretense than that of others. I argue that we are especially likely to notice pretense when we’re made self-conscious, and that failing to match the stereotype of one’s profession or domain of expertise tends to give rise to self-consciousness. Self-consciousness is elicited by certain cues; this fact explains not only the greater vulnerability of the members of some groups to it, but also explains why the empirical data has not yet provided evidence for this greater vulnerability. I end with some suggestions for reducing the prevalence or impact of impostor syndrome. (shrink)

Background. Imposter syndrome, associated with self-doubt and fear despite clear accomplishments and competencies, is frequently detected in medical students and has a negative impact on their well-being. This study aimed to predict the students’ IS using the machine learning ensemble approach. Methods. This study was a cross-sectional design among medical students in Bangladesh. Data were collected from February to July 2020 through snowball sampling technique across medical colleges in Bangladesh. In this study, we employed three different machine learning techniques such (...) as neural network, random forest, and ensemble learning to compare the accuracy of prediction of the IS. Results. In total, 500 students completed the questionnaire. We used the YIS scale to determine the presence of IS among medical students. The ensemble model has the highest accuracy of this predictive model, with 96.4%, while the individual accuracy of random forest and neural network is 93.5% and 96.3%, respectively. We used different performance matrices to compare the results of the models. Finally, we compared feature importance scores between neural network and random forest model. The top feature of the neural network model is Y7, and the top feature of the random forest model is Y2, which is second among the top features of the neural network model. Conclusions. Imposter syndrome is an emerging mental illness in Bangladesh and requires the immediate attention of researchers. For instance, in order to reduce the impact of IS, identifying key factors responsible for IS is an important step. Machine learning methods can be employed to identify the potential sources responsible for IS. Similarly, determining how each factor contributes to the IS condition among medical students could be a potential future direction. (shrink)