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. Author manuscript; available in PMC: 2024 Jul 1.
Published in final edited form as: J Am Coll Radiol. 2023 Mar 9;20(7):712–718. doi: 10.1016/j.jacr.2023.01.007

Table 2.

Multivariate logistic regression models for burnout

Parameter Level Odds Ratio 95% Confidence Interval Overall P Value
Age <66 y .0091
≥66 y 0.175 0.038-0.811
Take call None .0215
Yes, but no weekends 1.039 0.274-3.939
Yes, including weekends 2.527 0.910-7.019
Other 0.398 0.038-4.145
Last physical <1 y .8809
1 to >5 y 1.067 0.614-1.855
Never 1.306 0.452-3.771

Table 2 summarizes results from multivariate logistic regression model 1 for the probability that a radiologist experiences burnout (see text for definition). This model categorizes “take call” into four categories and presents the odds ratio, 95% confidence interval, and P value for each of the parameters of interest. Age and taking calls were significantly associated with burnout. Younger radiologists were more likely to experience burnout. Radiologists who took calls were more likely to be burned out than those who did not.