If you use a >4 threshold you could indeed say that Q5, Q6, Q7 are multicollinear and the other ones are ok. >4 is the most conservative threshold out there. As indicated many use >5 or even >10.
I think your solution is awkward. Running a PCA model on just those three variables will not work well. PCA is better catered when you have a bunch of variables that are multicollinear and you want to reduce them to just three indexes-like variables (3 principal components).
If you cared to, you could run a PCA with all 10 variables and derive the three principal component-variables that would pretty much retain the information in your model. However, PCA models get very opaque as the principal components often are difficult to interpret. So, I do not necessarily recommend going the PCA route.
I think your solution is rather qualitative in nature. First, look at your sample. Unless your sample is reasonably large (let's say over several hundred data points), I suspect you have way too many variables in this model. So, focus on the 5 variables that have the most explanatory meaning for what you are trying to estimate and go with that. Obviously, the Q5/Q6/Q7 variables appear somewhat redundant. You probably need to keep only one of those or maybe none at all.
So, the key is really to focus on what your variables mean and which one makes the most sense to keep in the model in order to explain your dependent variable.