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On average, a resident would see between 5 and 10 faculty members in a faculty, typically in one-hour intervals, lasting one to two years. Therefore, to maximize the influence of faculty’s teaching performance on being seen as a role model, a multistage analytical process was used (see Methods ) to construct a conceptual framework and interpret the data: (i) a logistic regression was fitted to the data to explore the effect of faculty’s teaching performance on each of the distinct role models, (ii) statistically-significant predictors (p<0.05) were identified and their interactions were studied by fitting a logistic regression for each combination of two predictors, and (iii) a stepwise approach was used to select the predictors and interactions that best fit the data and other factors. The goal was to construct an explanatory model for the relationship between faculty's teaching performance and the role models. The number of interaction terms of the variables (in our analysis, two variables from the same domain and one pair of variables from different domains) is limited and is therefore not a potential concern. Finally, we used statistical analyses to determine how the impact of faculty's teaching performance depended on the discrete properties of the role models.
Results are reported in relation to the two theoretical models: the teacher model (figure 2 ) and the physician model (figure 3 ) (see box S2 for a summary of the analysis). Data suggest a close relationship between the teaching performance and the teacher/supervisor and person models (figure 2, figure 3 ), but not the physician model. This is notable because the physician model was based only on the aggregated data from the physician respondents (without the interactions) and therefore might not fully reflect their real-world functioning. However, after taking account of the interactions between these predictors, we still found that the physician model was not as influential as the teacher and person models. What is more, the specificity of the physician role varied greatly across specialties. To better understand these findings, we also performed analyses separately for the specialty model and for the physician model, where interactions between the predictors and the specialty were studied.
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