I found that confusing, too. I guess that's the best you can expect if you train your model in such an unrealistically balanced dataset. Put it differently. If I had 100% accuracy at detecting depression in a dataset of depressed people, how well will run this "detector" at detecting depression in the real world? Well, being 'd' the incidence of depression:

P(being depressed | trivial detector said so) = d * 1.0 / 1.0 = d