Non linear, sequential model using results of cumulative regression in a bernoulli regression

Hi all,
I was interested in modeling something like this (but I don’t realy know how to categorise/name it): I have data about the descission for or against keeping a testitem in the itempool after a expert rating made by the head of study in binary form. I want to do a logistic regression for this process to get a hint what argument might have been most important for this descission.

The arguments include:the importance of the item (as seen by the experts) and if the experts found the items to be problematic in some way. The importance was rated on a rating scale.

So the base formula looks something like this using brmsfamily(“bernoulli”, “logit”):

resp_desc ~ 1 + impotance + error

Now my idea was to model the importance rating of an item with the cumulative link via based on the data given by the experts

importance_rating ~ 1 + (1 | item)

and use these values for the item’s importance in the first formula in the importance term. This seems to require some kind of sequential model fitting in each step. First the values for the items importance have to be estimated. And then the logistic regression on the descissions could follow. Is something like this possible? Or is it possible to do this simultaneousely and combine both fomulae?

Sincerely Simon Schäfer